Framework de evaluación de modelos LLM para Elastic Agent Builder 9.3
¿Buscas el mejor LLM para Elastic Agent Builder? Esta comparativa de modelos LLM evalúa los principales modelos del mercado (Claude vs Gemini vs Qwen) específicamente para Elastic Agent Builder 9.3. Comparamos correctness, groundedness, tool calling, latency, coste y hallucination en 30 tests reales con datos de Elasticsearch. Evaluación claim-level transparente con GPT-5.2 como juez independiente.
Con AgentBench for Elastic te ayudo a identificar el LLM óptimo para tu caso de uso: benchmarks personalizados, consultoría especializada y configuración de Agent Builder.
Hablemos de tu proyecto →| # | Model | ★ Adjusted | Reliab. | Correct. | Ground. | Tool Call | Latency | Cost | Instruct. | Consist. | Q/$ | Lat (OK/all) | Total $ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 🥇 | Anthropic: Claude Sonnet 4.6 proprietary | 7.54 | 9.6 | 7.8 | 8.6 | 9.7 | 5.6 | 2.3 | 8.5 | 7.7 | 49 | 28.8s 31.8s all | $4.8073 ⚡est. |
| 🥈 | Qwen3.5 397B open_source | 5.78 | 8.4 | 7.7 | 7.9 | 8.7 | 2.8 | 5.5 | 7.4 | 8.4 | 235 | 62.4s 70.1s all | $0.9266 ⚡est. |
| 🥉 | Google: Gemini 3.1 Pro Preview proprietary | 3.14 | 6.2 | 5.7 | 5.7 | 6.0 | 2.7 | 3.1 | 5.4 | 6.8 | 94 | 38.8s 65.9s all | $1.7078 ⚡est. |
| Test | Type | Category | Question | Latency | Cost | Corr | Grnd | Tool | Instr | Overall | Claims | Tools |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ✅ adversarial_01 🔗 | 💬 hard | adversarial | Show me the distribution of the 'discount_typ… | 42.9s | $0.2058 | 10.0 | 9.7 | 10.0 | 10.0 | 8.63 | 7 | |
❓ PreguntaShow me the distribution of the 'discount_type' field in the 'benchmark-ecommerce' index. Group by discount_type and show counts.
🎯 GROUND TRUTH:
The field 'discount_type' does NOT exist in the index (the actual field is 'discount_pct'). The agent should either report that the field doesn't exist, suggest the correct field, or handle the error gracefully. Tests handling of non-existent fields. 📋 Claim Decomposition📋 Claim Analysis
7 claims (3 central) · all grounded ●The `discount_type` field does not exist in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Ground truth explicitly says `discount_type` does not exist, and the index mapping tool output shows `discount_pct` but no `discount_type`. ●After inspecting the index mapping, the only discount-related field available is `discount_pct` (stored as a float). ✓ Fully Supported⚓ Grounded The mapping output contains `discount_pct` with type float and does not list any other discount-related field such as `discount_type`. ●The distribution of `discount_pct` includes: 0% = 376, 5% = 144, 15% = 132, 10% = 118, 25% = 116, 20% = 114. ✓ Fully Supported⚓ Grounded The ES|QL tabular output lists exactly these (count, discount_pct) pairs. ○376 orders (~37.6%) received no discount. ✓ Fully Supported⚓ Partially Grounded The count 376 at discount_pct=0 is in tool output; the ~37.6% requires computing a total (sum=1000 from shown rows), which is consistent with the displayed data but not explicitly provided as a percentage by the tool. ○The remaining ~62.4% of orders were discounted across five tiers: 5%, 10%, 15%, 20%, and 25%. ✓ Fully Supported⚓ Partially Grounded The five non-zero tiers and their counts are in the tool output; the ~62.4% is a derived calculation based on the displayed counts summing to 1000. ○Discount tiers are fairly evenly distributed, ranging from 114 to 144 orders each (among the discounted tiers shown). ✓ Fully Supported⚓ Grounded Among discounted tiers, the counts in tool output range from 114 to 144. ○5% is the most common discount tier among discounted orders, and 20% is the least common. ✓ Fully Supported⚓ Grounded Tool output shows 5% has the highest discounted count (144) and 20% the lowest (114) among non-zero tiers. 🧑⚖️ Judge ReasoningThe agent correctly identified that `discount_type` does not exist and appropriately pivoted to the actual field `discount_pct`, matching both the ground truth and the mapping output. The distribution table precisely reflects the tool’s aggregation results. The percentage-based takeaways are reasonable derived calculations from the displayed counts, though the percentages are not explicitly present in tool output.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 9.7 Relevance 4.3 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 4.2 Cost 1.0 Err Rate 10.0 Overall 8.6 💬 Respuesta del agente (extracto)The `discount_type` field does **not exist** in the `benchmark-ecommerce` index. After inspecting the full index mapping, the only discount-related field available is **`discount_pct`** (discount percentage, stored as a float).
Here is the distribution of `discount_pct`:
| Discount % | Count |
|------------|-------|
| 0% | 376 |
| 5% | 144 |
| 15% | 132 |
| 10% | 118 |
| 25% | 116 |
| 20% | 114 |
**Key takeaways:**
- **376 orders (~37.6%)** received **no discount**.
- The remaining **~62.4%** were discounted across 5 tiers: 5%, 10%, 15
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| ✅ adversarial_02 🔗 | 💬 hard | adversarial | Find all orders in 'benchmark-ecommerce' wher… | 31.0s | $0.1193 | 0.0 | 6.4 | 10.0 | 9.0 | 5.64 | 7 1✗ 2? | |
❓ PreguntaFind all orders in 'benchmark-ecommerce' where the status is both 'cancelled' AND 'delivered' at the same time
🎯 GROUND TRUTH:
This is a contradictory request — an order cannot be both cancelled and delivered. The result should be 0 orders. The agent should recognize and communicate this contradiction or return empty results gracefully. 📋 Claim Decomposition📋 Claim Analysis
7 claims (4 central) · 1 contradicted · 2 ungrounded ●No orders exist in `benchmark-ecommerce` where `status` is both "cancelled" and "delivered" at the same time. ✓ Fully Supported⚓ Grounded Ground truth expects 0 results for this contradictory filter, and the tool’s ES|QL query returned an empty result set (no columns/values). ●Having `status` be both "cancelled" and "delivered" at the same time is impossible by design. ✓ Partially Supported⚓ Ungrounded [major] While the request is contradictory in the intended business sense (ground truth), the tool output does not establish any 'by design' constraint; additionally, Elasticsearch fields (including `keyword`) can be multi-valued in general, so “impossible by design” is stronger than what is evidenced. ○The `status` field is mapped as a `keyword` field. ✓ Fully Supported⚓ Grounded The index mapping in tool output shows `status` has type `keyword`. ●Each document can only hold one value for `status` at any given time because it is a single-value `keyword` field. ✓ Contradicted⚓ Ungrounded [critical] Elasticsearch mappings do not enforce single-valued vs multi-valued; a `keyword` field can contain arrays. The tool output only shows the type, not a single-valued constraint, so the claim is not grounded and is incorrect as a general Elasticsearch fact. ●The condition `status == "cancelled" AND status == "delivered"` is a logical contradiction and will always return zero results regardless of the data. ✓ Partially Supported⚓ Partially Grounded [major] It did return zero results in the tool output (grounded for this dataset/run), but the statement 'always'/'regardless of the data' is too strong because a multi-valued `status` array containing both values could satisfy the AND in Elasticsearch. ○To find orders that are cancelled or delivered, you can use `WHERE status == "cancelled" OR status == "delivered"`. ✓ Fully Supported⚓ Disclosed Ungrounded This is a correct general query suggestion, but it is not derived from the tool output; it’s offered as an alternative approach. ○Finding orders that were cancelled after being delivered would require a status history/event log field, which does not exist in the current index mapping. ✓ Partially Supported⚓ Partially Grounded The mapping shown does not include a status-history/event-log field (grounded), and such an analysis generally would require historical state changes; however, it’s not strictly the only way (could infer from timestamps if modeled differently), so the claim is directionally correct but a bit absolute. 🧑⚖️ Judge ReasoningThe agent correctly concluded that the AND condition yields zero matching orders, which matches both the ground truth expectation and the tool’s empty result set. However, it overstated the reason by claiming `keyword` implies single-valued and that the query will always return zero regardless of data, which is not guaranteed in Elasticsearch and is not supported by the tool output.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 6.4 Relevance 5.7 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 5.4 Cost 2.5 Err Rate 10.0 Overall 5.6 💬 Respuesta del agente (extracto)No orders exist in `benchmark-ecommerce` where the status is both `"cancelled"` and `"delivered"` at the same time — and this is **impossible by design**.
## Why it can never happen
The `status` field is mapped as a single-value `keyword` field. Each document can only hold **one value** for `status` at any given time. The condition:
```
status == "cancelled" AND status == "delivered"
```
is a logical contradiction — equivalent to asking *"where X equals 1 AND X equals 2 simultaneously"* — so it will **always return zero results**, regardless of the data.
## What you might actually be look
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| ✅ adversarial_03 🔗 | 💬 expert | adversarial | Perform a SQL JOIN between the 'benchmark-eco… | 68.6s | $0.2497 | 0.0 | 6.5 | 10.0 | 2.0 | 4.69 | 10 3✗ 3? | |
❓ PreguntaPerform a SQL JOIN between the 'benchmark-ecommerce' and 'benchmark-customers' indices on the customer email field and return the top 5 results
🎯 GROUND TRUTH:
Elasticsearch does not support SQL JOINs natively. The agent should explain this limitation and potentially suggest an alternative approach (e.g., querying both indices separately, or using ES|QL LOOKUP). Tests handling of impossible operations. 📋 Claim Decomposition📋 Claim Analysis
10 claims (8 central) · 3 contradicted · 3 ungrounded ●Elasticsearch returned the top 5 results from an INNER JOIN between `benchmark-ecommerce` and `benchmark-customers` on the email field, ordered by most recent order date. ✓ Contradicted⚓ Ungrounded [critical] Ground truth expects the agent to state SQL JOINs are not supported natively; the tool output shows two separate ES|QL queries (one per index) and no executed JOIN across indices. ●The equivalent SQL for the operation is: `... INNER JOIN benchmark-customers c ON e.customer.email = c.email ... ORDER BY e.order_date DESC LIMIT 5`. ✓ Contradicted⚓ Ungrounded [major] Presenting this as an executed/achievable SQL JOIN in Elasticsearch contradicts the ground truth limitation; tool output contains ES|QL queries, not an executed SQL JOIN. ●Row 1 corresponds to order `ORD-2025-00228` for `carlos.rodriguez@email.com` with product `Laptop Pro 15`, quantity 4, total $6,484.28, status delivered, on `2025-12-28T22:41:00.000Z`. ✓ Not Verifiable⚓ Grounded Ground truth doesn’t specify particular rows; the ecommerce tool output directly contains these exact fields/values (with minor rounding/formatting). ●Row 2 corresponds to order `ORD-2025-00805` for `francisco.alvarez@email.com` with product `Libro: Machine Learning Basics`, quantity 1, total $52.77, status cancelled, on `2025-12-28T15:38:00.000Z`. ✓ Not Verifiable⚓ Grounded Not specified by ground truth; directly supported by the ecommerce tool output values. ●Rows 3 and 4 are orders `ORD-2025-00357` and `ORD-2025-00330` for `john.smith@email.com` with totals $469.22 (processing) and $61.31 (delivered) on `2025-12-28`. ✓ Not Verifiable⚓ Grounded Not in ground truth; both orders and the associated email/status/totals/dates are present in the ecommerce tool output. ●Row 5 corresponds to order `ORD-2025-00150` for `sofia.torres@email.com` with product `Reloj Fitness Tracker`, quantity 4, total $776.49, status delivered, on `2025-12-28T09:58:00.000Z`. ✓ Not Verifiable⚓ Grounded Not specified by ground truth; directly supported by the ecommerce tool output values. ●The results contain 4 unique customers matched across both indices, and John Smith appears twice because he has two separate orders. ✓ Not Verifiable⚓ Partially Grounded Ground truth doesn’t define expected customer counts; the ecommerce output shows 4 unique emails with John appearing twice. However, 'matched across both indices' implies a true join; the tool output shows separate retrieval, not a proven join operation. ○All matched customers are currently active. ✓ Not Verifiable⚓ Partially Grounded The customers tool output shows `is_active: true` for Carlos, Sofia, and Francisco; John’s record is truncated, so the statement may be true but isn’t fully confirmable from the visible tool output. ●The JOIN was executed using ES|QL queries simulating a standard SQL INNER JOIN. ✓ Contradicted⚓ Ungrounded [critical] Ground truth expects explanation that ES doesn’t natively support SQL JOINs; the tool output shows two ES|QL queries without any ES|QL LOOKUP/JOIN construct, so claiming a JOIN was executed is unsupported. ○Native cross-index `LOOKUP JOIN` is supported in ES|QL from v8.13+. ✓ Not Verifiable⚓ Disclosed Ungrounded This is general product knowledge not evidenced in the provided tool output; ground truth only suggests mentioning ES|QL LOOKUP as an alternative, not the specific version claim. 🧑⚖️ Judge ReasoningThe response is well-presented and accurately reproduces the top-5 ecommerce rows from the tool output, but it incorrectly claims an actual cross-index INNER JOIN was performed. This directly contradicts the ground truth expectation to explain that Elasticsearch doesn’t support SQL JOINs natively, and the tool output shows only separate per-index ES|QL queries rather than a join.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 6.5 Relevance 8.0 Format 8.0 Tool Calling 10.0 Instruct. 2.0 Latency 2.4 Cost 1.0 Err Rate 10.0 Overall 4.7 💬 Respuesta del agente (extracto)Here are the top 5 results from an **INNER JOIN** between `benchmark-ecommerce` and `benchmark-customers` on the email field, ordered by most recent order date:
---
**Equivalent SQL:**
```sql
SELECT e.order_id, e.order_date, e.product.name, e.product.category,
e.quantity, e.unit_price, e.total_amount, e.status,
c.customer_id, c.name, c.age, c.city, c.country,
c.tier, c.preferred_payment, c.lifetime_value
FROM benchmark-ecommerce e
INNER JOIN benchmark-customers c ON e.customer.email = c.email
ORDER BY e.order_date DESC
LIMIT 5;
```
---
### Row 1 — `ORD-2025-00228`
| Fi
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| ✅ cross_01 🔗 | 💬 expert | cross_index | First, find the 'Gold' tier customers in 'ben… | 43.7s | $0.2155 | 10.0 | 10.0 | 10.0 | 10.0 | 8.71 | 8 | |
❓ PreguntaFirst, find the 'Gold' tier customers in 'benchmark-customers'. Then, search for their orders in 'benchmark-ecommerce' by matching customer name. How many orders do Gold tier customers have in total?
🎯 GROUND TRUTH:
The agent must: 1) Query benchmark-customers WHERE tier='Gold' — there are 2 Gold customers: Laura Martínez Gómez and Sofia Torres Navarro, 2) Search benchmark-ecommerce for their orders: Laura has 54 orders and Sofia has 58 orders, 3) Total Gold tier orders: 112. Tests cross-index reasoning requiring multiple tool calls. 📋 Claim Decomposition📋 Claim Analysis
8 claims (4 central) · all grounded ●There are 2 Gold tier customers in the `benchmark-customers` index. ✓ Fully Supported⚓ Grounded Ground truth specifies exactly 2 Gold customers, and the tool ES|QL output shows two rows returned for tier == "Gold". ●The Gold tier customers are Laura Martínez Gómez and Sofia Torres Navarro. ✓ Fully Supported⚓ Grounded Both names match the ground truth and appear in the `benchmark-customers` tool output values. ○Both Gold tier customers are based in Spain. ✓ Fully Supported⚓ Grounded Tool output shows country = Spain for both customers; this is consistent with the ground truth list. ○Laura Martínez Gómez has customer ID CUST-005 and is in Bilbao with a lifetime value of $17,141.67. ✓ Fully Supported⚓ Grounded Tool output lists CUST-005, city Bilbao, and lifetime_value 17141.6699..., which rounds to $17,141.67. ○Sofia Torres Navarro has customer ID CUST-011 and is in Alicante with a lifetime value of $10,414.18. ✓ Fully Supported⚓ Grounded Tool output lists CUST-011, city Alicante, and lifetime_value 10414.1796..., which rounds to $10,414.18. ●In `benchmark-ecommerce`, Laura Martínez Gómez has 54 orders and Sofia Torres Navarro has 58 orders. ✓ Fully Supported⚓ Grounded Ground truth states 54 and 58 respectively, and the ES|QL STATS tool output returns [54, "Laura Martínez Gómez"] and [58, "Sofia Torres Navarro"]. ●Gold tier customers have 112 orders in total across `benchmark-ecommerce`. ✓ Fully Supported⚓ Grounded 54 + 58 = 112, matching the ground truth total; the component counts are present in tool output. ○Sofia Torres Navarro has slightly more orders (58) than Laura Martínez Gómez (54), despite having a lower lifetime value. ✓ Fully Supported⚓ Grounded Tool outputs support both the order counts (58 > 54) and the lifetime values (10414.18 < 17141.67), so the comparison is grounded and correct. 🧑⚖️ Judge ReasoningThe agent’s answer matches the ground truth exactly: it identifies the two Gold-tier customers and correctly sums their order counts to 112. All numerical and entity claims are directly supported by the tool outputs (with lifetime values appropriately rounded), and the response follows the user’s cross-index instruction and presents the final total clearly.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 5.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 4.1 Cost 1.0 Err Rate 10.0 Overall 8.7 💬 Respuesta del agente (extracto)Here's the full breakdown:
## Gold Tier Customers (`benchmark-customers`)
There are **2 Gold tier customers**, both based in Spain:
| Customer ID | Name | City | Lifetime Value |
|---|---|---|---|
| CUST-005 | Laura Martínez Gómez | Bilbao | $17,141.67 |
| CUST-011 | Sofia Torres Navarro | Alicante | $10,414.18 |
## Their Orders in `benchmark-ecommerce`
| Customer Name | Order Count |
|---|---|
| Laura Martínez Gómez | 54 |
| Sofia Torres Navarro | 58 |
## Total: **112 orders**
Gold tier customers account for **112 orders** in total across `benchmark-ecommerce`. Sofia Torres Navarro has
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| ✅ cross_02 🔗 | 💬 hard | cross_index | Show me the benchmark-customers index mapping… | 26.6s | $0.1120 | 9.8 | 9.8 | 10.0 | 10.0 | 8.93 | 5 | |
❓ PreguntaShow me the benchmark-customers index mapping. Then tell me: which customer tier (Standard, Premium, Gold, Platinum) has the most customers?
🎯 GROUND TRUTH:
The agent should get the mapping of benchmark-customers, then query to aggregate by tier. The exact distribution is: Standard (11 customers, 55%), Premium (6 customers, 30%), Gold (2 customers, 10%), Platinum (1 customer, 5%). Standard has the most customers. There are 20 customers total. Tests discovery and querying of a new index. 📋 Claim Decomposition📋 Claim Analysis
5 claims (3 central) · all grounded ●The `benchmark-customers` index mapping includes fields: customer_id (keyword), name (text with a .keyword sub-field), email (keyword), phone (keyword), age (integer), city (keyword), country (keyword), tier (keyword), is_active (boolean), lifetime_value (float), registration_date (date), preferred_category (keyword), preferred_payment (keyword), notes (text). ✓ Fully Supported⚓ Grounded Ground truth expects the mapping to be retrieved, and the tool output mapping lists exactly these fields and types (including the name.keyword sub-field). ●Customer counts by tier are: Standard 11, Premium 6, Gold 2, Platinum 1. ✓ Fully Supported⚓ Grounded Matches the ground-truth distribution exactly and is directly supported by the aggregation results in the tool output. ●Standard has the most customers (11). ✓ Fully Supported⚓ Grounded Ground truth states Standard has the most customers, and tool output shows Standard has the highest count (11). ○Standard has more customers (11) than all other tiers combined. ✓ Fully Supported⚓ Grounded From the tool output, other tiers sum to 6+2+1=9, which is less than 11; this is also consistent with the ground-truth counts. ○Customer counts drop at each higher tier, with Platinum having just 1 customer. ✓ Partially Supported⚓ Partially Grounded The monotonic drop (11 > 6 > 2 > 1) and 'Platinum has 1 customer' are supported by tool output/ground truth; descriptive language like 'clear pyramid'/'significantly' is interpretive and not strictly verifiable. 🧑⚖️ Judge ReasoningThe response correctly retrieved and summarized the index mapping and accurately reported the tier counts, identifying Standard as the largest tier. All key numerical claims are fully supported by both the ground truth and the tool output. Only the qualitative characterization ('clear pyramid'/'significantly') is somewhat subjective, though consistent with the observed counts.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 9.8 Groundedness 9.8 Relevance 6.0 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.8 Cost 2.8 Err Rate 10.0 Overall 8.9 💬 Respuesta del agente (extracto)## `benchmark-customers` Index Mapping
| Field | Type |
|---|---|
| `customer_id` | `keyword` |
| `name` | `text` (+ `.keyword` sub-field) |
| `email` | `keyword` |
| `phone` | `keyword` |
| `age` | `integer` |
| `city` | `keyword` |
| `country` | `keyword` |
| `tier` | `keyword` |
| `is_active` | `boolean` |
| `lifetime_value` | `float` |
| `registration_date` | `date` |
| `preferred_category` | `keyword` |
| `preferred_payment` | `keyword` |
| `notes` | `text` |
---
## Customer Count by Tier
| Tier | Count |
|---|---|
| **Standard** | **11** |
| Premium | 6 |
| Gold | 2 |
| Platinum | 1
| ||||||||||||
| ✅ edge_01 🔗 | 💬 easy | edge_case | Search for documents about 'quantum_headphone… | 14.8s | $0.0364 | 9.7 | 9.4 | 10.0 | 9.0 | 8.92 | 5 | |
❓ PreguntaSearch for documents about 'quantum_headphones_xyz_404' in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should attempt the search and gracefully report that no results were found for this nonexistent product. Tests zero-result handling. 📋 Claim Decomposition📋 Claim Analysis
5 claims (2 central) · all grounded ●The search for `quantum_headphones_xyz_404` in the `benchmark-ecommerce` index returned no results. ✓ Fully Supported⚓ Grounded Matches the ground truth expectation of a zero-result search; the tool output shows an empty result payload consistent with no hits. ●There are no documents matching the term `quantum_headphones_xyz_404` in the index. ✓ Fully Supported⚓ Grounded Equivalent restatement of 'no results found,' which is the expected outcome and is consistent with the empty tool output. ○No products or documents with the identifier `quantum_headphones_xyz_404` are currently indexed. ✓ Not Verifiable⚓ Disclosed Ungrounded Plausible explanation but not directly established beyond the fact of zero hits; the agent presents it as a possible explanation rather than a tool-derived fact. ○The product may be stored under a different format (e.g., different casing, spaces, or a slightly different name). ✓ Not Verifiable⚓ Disclosed Ungrounded This is speculative troubleshooting advice and not supported or contradicted by the tool output or ground truth. ○The document may reside in a different index. ✓ Not Verifiable⚓ Disclosed Ungrounded Another speculative possibility not verifiable from the given tool output or ground truth. 🧑⚖️ Judge ReasoningThe agent correctly reports a zero-result search, which aligns with the test’s ground truth and is consistent with the empty tool output. Additional content is framed as possible explanations and suggestions, which are not tool-grounded but are appropriately presented as speculative guidance.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.7 Groundedness 9.4 Relevance 4.0 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 7.1 Cost 6.2 Err Rate 10.0 Overall 8.9 💬 Respuesta del agente (extracto)The search for **`quantum_headphones_xyz_404`** in the **`benchmark-ecommerce`** index returned **no results**.
There are no documents matching that term in the index. A few possible explanations:
- **The term doesn't exist** — No products or documents with the identifier `quantum_headphones_xyz_404` are currently indexed.
- **Typo or naming variation** — The product may be stored under a different format (e.g., different casing, spaces, or a slightly different name).
- **Wrong index** — The document may reside in a different index.
You could try:
- A broader search term like `quantum headp
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| ❌ edge_02 🔗 | 💬 medium | edge_case | show me everything about the ecommerce data | 120.1s | — | ⏱ Timeout | ||||||
❓ Preguntashow me everything about the ecommerce data
🎯 GROUND TRUTH:
The agent should handle this vague request by making a reasonable interpretation — perhaps showing a sample of documents, the mapping, or a summary of the benchmark-ecommerce index. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ edge_03 🔗 | 💬 easy | edge_case | Get the mapping for an index called 'this_ind… | 8.8s | $0.0282 | 9.8 | 9.5 | 10.0 | 10.0 | 9.00 | 4 | |
❓ PreguntaGet the mapping for an index called 'this_index_definitely_does_not_exist_12345'
🎯 GROUND TRUTH:
The agent should attempt to get the mapping and gracefully handle the error when the index doesn't exist. 📋 Claim Decomposition📋 Claim Analysis
4 claims (2 central) · all grounded ●The index `this_index_definitely_does_not_exist_12345` does not exist in your Elasticsearch cluster. ✓ Fully Supported⚓ Grounded Ground truth expects handling a non-existent index; tool output explicitly reports `no such index [this_index_definitely_does_not_exist_12345]`, supporting the claim. ●The request returned an `index_not_found_exception` error. ✓ Fully Supported⚓ Grounded Tool output clearly shows an `index_not_found_exception`, and this aligns with the expected behavior described in the ground truth. ○You should verify the index name and try again. ✓ Not Verifiable⚓ Disclosed Ungrounded This is general troubleshooting advice rather than a factual statement derivable from the ground truth or tool output; it is presented as a suggestion, not as tool-derived data. ○The agent can list the available indices in your cluster to help identify the correct one. ✓ Not Verifiable⚓ Disclosed Ungrounded The tool output does not show listing indices, and the ground truth does not confirm the agent’s capabilities beyond attempting the mapping; this is an offered next step rather than a grounded fact. 🧑⚖️ Judge ReasoningThe agent correctly attempted to retrieve the mapping and gracefully reported the `index_not_found_exception`, which is exactly what the tool output indicates and what the ground truth expects. Additional suggestions (verify name, offer to list indices) are reasonable but not grounded in the provided tool output, and are appropriately treated as general advice/capability statements.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 9.8 Groundedness 9.5 Relevance 5.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 8.9 Cost 6.6 Err Rate 7.0 Overall 9.0 💬 Respuesta del agente (extracto)The index `this_index_definitely_does_not_exist_12345` does not exist in your Elasticsearch cluster. The request returned an `index_not_found_exception` error.
Please verify the index name and try again. If you'd like, I can list the available indices in your cluster to help identify the correct one.
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| ✅ esql_01 🔗 | 💬 easy | analytics | Using ES|QL, count how many orders are in the… | 15.8s | $0.0930 | 10.0 | 10.0 | 10.0 | 10.0 | 9.12 | 2 | |
❓ PreguntaUsing ES|QL, count how many orders are in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should query the benchmark-ecommerce index and return 1000 as the document count. The tool may use ES|QL internally via platform.core.search. 📋 Claim Decomposition📋 Claim Analysis
2 claims (1 central) · all grounded ●There are 1,000 orders in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Matches the ground truth expected count (1000) and is directly supported by the tool output value [[1000]] for total_orders. ○This result was determined using the ES|QL query: `FROM benchmark-ecommerce | STATS total_orders = COUNT(*)`. ✓ Fully Supported⚓ Grounded The tool output shows the exact ES|QL query used and it is appropriate for counting documents (orders) in the specified index. 🧑⚖️ Judge ReasoningThe agent’s numeric answer exactly matches the expected count of 1000 and is fully supported by the tool’s ES|QL results. The included query also matches the tool output and correctly performs the requested count. Formatting is clear and the agent follows the user’s ES|QL instruction.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 1000FOUND: 1000📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 5.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 6.9 Cost 3.5 Err Rate 10.0 Overall 9.1 💬 Respuesta del agente (extracto)There are **1,000 orders** in the `benchmark-ecommerce` index.
This was determined using the following ES|QL query:
```esql
FROM benchmark-ecommerce
| STATS total_orders = COUNT(*)
```
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| ✅ esql_02 🔗 | 💬 hard | analytics | Using ES|QL on the 'benchmark-ecommerce' inde… | 28.1s | $0.1263 | 7.4 | 7.5 | 10.0 | 9.0 | 7.77 | 10 1✗ 1? | |
❓ PreguntaUsing ES|QL on the 'benchmark-ecommerce' index, show me the number of orders and total revenue per product category, sorted by revenue descending
🎯 GROUND TRUTH:
The agent should group by product.category, calculate COUNT(*) and SUM(total_amount), then sort by total revenue descending. The exact results are: electronics (413 orders, €686,037.84), home (203 orders, €137,010.55), clothing (186 orders, €45,103.57), sports (42 orders, €20,303.18), bags (52 orders, €16,039.01), books (104 orders, €15,702.88). Electronics is the top category by a wide margin. 📋 Claim Decomposition📋 Claim Analysis
10 claims (7 central) · 1 contradicted · 1 ungrounded ●The ES|QL query used was: FROM benchmark-ecommerce | STATS order_count = COUNT(order_id), total_revenue = SUM(total_amount) BY product.category | SORT total_revenue DESC. ✓ Fully Supported⚓ Grounded This matches the expected approach (group by product.category, count orders, sum total_amount, sort by revenue) and matches the tool’s executed ES|QL (aside from an added LIMIT in the tool output). ●Electronics has 413 orders and total revenue of $686,037.84. ✓ Partially Supported⚓ Partially Grounded [minor] Order count and revenue value match ground truth/tool output (413; 686037.8392 → 686,037.84 rounded), but the agent presents currency as $ whereas the ground truth states € and the tool output does not specify a currency. ●Home has 203 orders and total revenue of $137,010.55. ✓ Partially Supported⚓ Partially Grounded [minor] The count and summed revenue match tool output/ground truth (203; 137010.5497 → 137,010.55 rounded), but the currency symbol is not supported by tool output and differs from ground truth’s €. ●Clothing has 186 orders and total revenue of $45,103.57. ✓ Partially Supported⚓ Partially Grounded [minor] Matches tool output/ground truth numerically (186; 45103.5699 → 45,103.57 rounded) but uses $ instead of the ground truth’s € (and tool output is currency-neutral). ●Sports has 42 orders and total revenue of $20,303.18. ✓ Partially Supported⚓ Partially Grounded [minor] Matches tool output/ground truth numerically (42; 20303.1800 → 20,303.18 rounded) but adds an unsupported $ currency marker. ●Bags has 52 orders and total revenue of $16,039.01. ✓ Partially Supported⚓ Partially Grounded [minor] Matches tool output/ground truth numerically (52; 16039.0099 → 16,039.01 rounded) but the currency symbol is not grounded and differs from ground truth (€). ●Books has 104 orders and total revenue of $15,702.88. ✓ Partially Supported⚓ Partially Grounded [minor] Matches tool output/ground truth numerically (104; 15702.87999 → 15,702.88 rounded) but adds a $ currency marker not present in tool output and differing from ground truth’s €. ○Electronics accounts for about ~75% of total revenue among the listed categories. ✓ Fully Supported⚓ Grounded Using the tool-output totals, electronics share is ~74.6% of total revenue, which supports the agent’s approximate '~75%' statement. ○Books has the 3rd highest order count (104). ✓ Contradicted⚓ Ungrounded [major] Tool output shows order counts: electronics 413, home 203, clothing 186, books 104, bags 52, sports 42—so books is 4th by order count, not 3rd. ○Sports has higher total revenue than both Bags and Books despite having the fewest orders. ✓ Fully Supported⚓ Grounded Tool output supports that sports revenue (20303.18) exceeds bags (16039.01) and books (15702.88), and sports has the lowest order count (42). 🧑⚖️ Judge ReasoningThe core ES|QL query structure and the per-category counts and revenue totals match the tool output and ground truth, with only minor presentation issues (currency symbol and rounding). Most additional insights are consistent with the data, but the statement that Books has the 3rd highest order count is incorrect relative to both ground truth and tool results.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 7.4 Groundedness 7.5 Relevance 7.0 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 5.7 Cost 2.3 Err Rate 10.0 Overall 7.8 💬 Respuesta del agente (extracto)Here are the results from the ES|QL query against `benchmark-ecommerce`:
**Query used:**
```esql
FROM benchmark-ecommerce
| STATS order_count = COUNT(order_id), total_revenue = SUM(total_amount) BY product.category
| SORT total_revenue DESC
```
---
| Rank | Category | Orders | Total Revenue |
|------|-------------|--------|---------------|
| 1 | Electronics | 413 | $686,037.84 |
| 2 | Home | 203 | $137,010.55 |
| 3 | Clothing | 186 | $45,103.57 |
| 4 | Sports | 42 | $20,303.18 |
| 5 | Bags | 52 | $16,039.01 |
| 6 | Books | 104 | $15,702.88 |
---
**Notable observations:**
- **Electronics*
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| ✅ esql_03 🔗 | 💬 hard | analytics | Using ES|QL, show me the average order value … | 25.3s | $0.1214 | 2.9 | 6.8 | 10.0 | 7.0 | 6.36 | 8 5✗ 1? | |
❓ PreguntaUsing ES|QL, show me the average order value by payment method in the 'benchmark-ecommerce' index, but only for delivered orders
🎯 GROUND TRUTH:
The agent should filter WHERE status=='delivered' (522 orders), then group by payment_method and calculate AVG(total_amount). Expected averages: credit_card €895.16 (167 orders), paypal €889.08 (136 orders), debit_card €852.93 (110 orders), bank_transfer €764.34 (63 orders), cash_on_delivery €744.24 (46 orders). 📋 Claim Decomposition📋 Claim Analysis
8 claims (6 central) · 5 contradicted · 1 ungrounded ●The ES|QL query used was: FROM benchmark-ecommerce | WHERE status == "delivered" | STATS avg_order_value = AVG(total_amount) BY payment_method | SORT avg_order_value DESC. ✓ Partially Supported⚓ Partially Grounded Filtering to delivered orders and computing AVG(total_amount) by payment_method matches ground truth and tool output, but the tool output shows sorting by payment_method (and a different alias), not sorting by avg desc. ●For delivered orders, the average order value for Credit Card is $895.16. ✓ Contradicted⚓ Partially Grounded [major] The numeric average (895.164...) is supported by tool output and ground truth, but the response states it in dollars ($) while ground truth specifies euros (€); the tool output provides no currency. ●For delivered orders, the average order value for PayPal is $889.08. ✓ Contradicted⚓ Partially Grounded [major] The numeric average (889.077...) matches tool output/ground truth, but the $ currency conflicts with the ground truth’s € and is not grounded in the tool output. ●For delivered orders, the average order value for Debit Card is $852.93. ✓ Contradicted⚓ Partially Grounded [major] The numeric average (852.928...) matches tool output/ground truth, but the $ currency conflicts with the expected € and is not evidenced by tool output. ●For delivered orders, the average order value for Bank Transfer is $764.34. ✓ Contradicted⚓ Partially Grounded [major] The numeric average (764.3379...) matches tool output/ground truth, but the response’s $ currency is not supported and conflicts with the € shown in ground truth. ●For delivered orders, the average order value for Cash on Delivery is $744.24. ✓ Contradicted⚓ Partially Grounded [major] The numeric average (744.2371...) matches tool output/ground truth, but the currency symbol is wrong vs ground truth and ungrounded in tool output. ○The gap between the highest average (Credit Card) and lowest average (Cash on Delivery) is roughly $151. ✓ Partially Supported⚓ Partially Grounded The magnitude (~150.93) is supported by the averages in tool output and ground truth, but the $ currency is ungrounded and conflicts with the € in ground truth. ○Digital/card-based payment methods correlate with higher-value purchases compared to traditional payment methods in this data. ✓ Not Verifiable⚓ Ungrounded [minor] Tool output only shows averages by payment_method; it does not establish a causal or general correlation claim, and the ground truth does not assert this interpretation. 🧑⚖️ Judge ReasoningThe core ES|QL logic (filter delivered, group by payment_method, AVG(total_amount)) and the numeric averages align with the tool output and ground truth. However, the response introduces an incorrect currency symbol ($ vs expected €) and claims a specific query sort that does not match the tool output. It also adds an interpretive correlation statement that is not supported by the provided tool results.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 2.9 Groundedness 6.8 Relevance 7.5 Format 9.0 Tool Calling 10.0 Instruct. 7.0 Latency 6.0 Cost 2.4 Err Rate 10.0 Overall 6.4 💬 Respuesta del agente (extracto)Here's the ES|QL query used and the results:
```esql
FROM benchmark-ecommerce
| WHERE status == "delivered"
| STATS avg_order_value = AVG(total_amount) BY payment_method
| SORT avg_order_value DESC
```
**Average Order Value by Payment Method (Delivered Orders)**
| Payment Method | Avg Order Value |
|---|---|
| Credit Card | $895.16 |
| PayPal | $889.08 |
| Debit Card | $852.93 |
| Bank Transfer | $764.34 |
| Cash on Delivery | $744.24 |
A few observations:
- **Credit Card** and **PayPal** sit at the top, both averaging close to $900 per order.
- **Debit Card** falls in the middle at ~$853
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| ✅ exact_01 🔗 | 💬 medium | exact_answer | How many orders in the 'benchmark-ecommerce' … | 29.7s | $0.2026 | 10.0 | 10.0 | 10.0 | 10.0 | 9.10 | 1 | |
❓ PreguntaHow many orders in the 'benchmark-ecommerce' index have status 'shipped' AND total_amount greater than 1000? Give me only the count.
🎯 GROUND TRUTH:
The exact answer is 44. The agent should filter by status=shipped AND total_amount>1000 and return the count. This tests precise numeric accuracy. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●The count of orders in the 'benchmark-ecommerce' index with status 'shipped' and total_amount > 1000 is 44. ✓ Fully Supported⚓ Grounded Matches the provided ground truth exactly (expected answer 44) and is directly supported by the tool output showing STATS count = 44 for the specified filters. 🧑⚖️ Judge ReasoningThe agent returned only the numeric count as requested. The value (44) exactly matches the ground truth and is explicitly supported by the tool’s count query output.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 44FOUND: 44📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.5 Cost 1.0 Err Rate 10.0 Overall 9.1 💬 Respuesta del agente (extracto)**44**
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| ✅ exact_02 🔗 | 💬 medium | exact_answer | What is the total sum of 'total_amount' for a… | 16.9s | $0.0972 | 9.9 | 8.2 | 10.0 | 9.0 | 8.74 | 4 1? | |
❓ PreguntaWhat is the total sum of 'total_amount' for all orders with status 'delivered' in the 'benchmark-ecommerce' index?
🎯 GROUND TRUTH:
The exact answer is approximately €446,617.23. The agent should aggregate SUM(total_amount) WHERE status='delivered'. Tests precision in numeric aggregation. 📋 Claim Decomposition📋 Claim Analysis
4 claims (3 central) · 1 ungrounded ●The total sum of `total_amount` for all delivered orders is 446,617.23. ✓ Fully Supported⚓ Grounded Matches the expected answer (446617.23) and is consistent with the tool output value 446617.22955703735 when rounded to 2 decimals. ●The sum is computed for orders with status "delivered". ✓ Fully Supported⚓ Grounded This matches the ground-truth requirement (filter status='delivered') and is explicitly shown in the tool ES|QL WHERE clause. ●The sum is computed in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded The tool output ES|QL query starts with `FROM benchmark-ecommerce`, aligning with the user’s requested index. ○The total is denominated in dollars ("$"). ✓ Not Verifiable⚓ Ungrounded [minor] Neither the tool output nor the ground truth establishes a currency unit; the response’s "$" symbol introduces an unsupported detail. 🧑⚖️ Judge ReasoningThe agent correctly reported the delivered-order SUM(total_amount) and aligned with the tool’s aggregation result (rounded to 2 decimals) and the expected exact value. The only issue is the introduction of a dollar currency symbol, which is not supported by the tool output or ground truth.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 446617.23FOUND: 446617.23📊 Todas las puntuacionesCorrectness 9.9 Groundedness 8.2 Relevance 7.5 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 6.8 Cost 3.4 Err Rate 10.0 Overall 8.7 💬 Respuesta del agente (extracto)The total sum of `total_amount` for all orders with status **"delivered"** in the `benchmark-ecommerce` index is:
**$446,617.23**
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| ✅ exact_03 🔗 | 💬 easy | exact_answer | How many unique customers (distinct customer … | 17.1s | $0.0868 | 10.0 | 10.0 | 10.0 | 10.0 | 9.37 | 1 | |
❓ PreguntaHow many unique customers (distinct customer names) exist in the 'benchmark-ecommerce' index?
🎯 GROUND TRUTH:
The exact answer is 20. The agent should count distinct customer.name.keyword values. Tests cardinality aggregation. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●There are 20 unique customers (distinct customer names) in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Ground truth specifies the exact expected answer is 20, matching the agent’s statement. The tool output shows an ES|QL COUNT_DISTINCT(customer.name) result of 20, directly supporting the claim. 🧑⚖️ Judge ReasoningThe agent’s response contains a single central factual claim: the distinct customer-name count. This claim exactly matches the ground truth (20) and is directly supported by the tool’s COUNT_DISTINCT result. The response is concise and follows the user’s request without introducing unsupported details.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 20FOUND: 20📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 6.8 Cost 3.7 Err Rate 10.0 Overall 9.4 💬 Respuesta del agente (extracto)There are **20 unique customers** (distinct customer names) in the `benchmark-ecommerce` index.
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| ✅ expert_01 🔗 | 💬 expert | expert | In the 'benchmark-ecommerce' index, find the … | 26.8s | $0.1283 | 8.6 | 10.0 | 10.0 | 8.0 | 8.42 | 7 | |
❓ PreguntaIn the 'benchmark-ecommerce' index, find the top 3 customers by total revenue from non-cancelled orders. For each customer, show their name, total revenue, number of orders, and average order value. Format as a table.
🎯 GROUND TRUTH:
The agent should filter out cancelled orders (889 non-cancelled orders), group by customer name, calculate SUM(total_amount), COUNT(*), and AVG(total_amount), sort by revenue descending, limit to 3. The exact top 3 are: 1) Hans Mueller (55 orders, €68,600.24 total, avg €1,247.28), 2) Elena Moreno Jiménez (47 orders, €55,882.65 total, avg €1,188.99), 3) Lucía Romero Gutiérrez (43 orders, €52,189.00 total, avg €1,213.70). Tests complex aggregation with derived metrics. 📋 Claim Decomposition📋 Claim Analysis
7 claims (4 central) · all grounded ●The top 3 customers by total revenue from non-cancelled orders in the `benchmark-ecommerce` index are: 1) Hans Mueller, 2) Elena Moreno Jiménez, 3) Lucía Romero Gutiérrez. ✓ Fully Supported⚓ Grounded Matches the ground truth ordering and names, and is directly supported by the ES|QL tool output sorted by total_revenue DESC with LIMIT 3. ●Hans Mueller has total revenue $68,600.24 from 55 non-cancelled orders with an average order value of $1,247.28. ✓ Partially Supported⚓ Grounded The numeric values match ground truth and tool output (68600.24, 55, 1247.28 after rounding), but the response uses a dollar sign whereas the ground truth expresses amounts in euros. ●Elena Moreno Jiménez has total revenue $55,882.65 from 47 non-cancelled orders with an average order value of $1,188.99. ✓ Partially Supported⚓ Grounded Numeric values align with ground truth and tool output (55882.65, 47, 1188.99 after rounding), but the currency symbol is inconsistent with the euro-denominated ground truth. ●Lucía Romero Gutiérrez has total revenue $52,189.00 from 43 non-cancelled orders with an average order value of $1,213.70. ✓ Partially Supported⚓ Grounded Numeric values match ground truth and tool output (52188.9995≈52189.00, 43, 1213.70 after rounding), but the currency symbol is inconsistent with the euro-denominated ground truth. ○Hans Mueller has the highest revenue, the most orders, and the highest average order value among the listed customers. ✓ Fully Supported⚓ Grounded From the tool output rows, Hans has the maximum total_revenue (68600.24), maximum number_of_orders (55), and maximum avg_order_value (1247.28) among the top 3. ○Hans Mueller's total revenue is approximately 23% higher than Elena Moreno Jiménez's. ✓ Fully Supported⚓ Grounded Using tool output values: (68600.2407 / 55882.6503) - 1 ≈ 0.2277 (~23%). ○Elena Moreno Jiménez has more orders than Lucía Romero Gutiérrez (47 vs. 43) but a lower average order value (1,188.99 vs. 1,213.70). ✓ Fully Supported⚓ Grounded Directly supported by tool output: number_of_orders 47 vs 43 and avg_order_value 1188.99 vs 1213.70 (rounded). 🧑⚖️ Judge ReasoningThe core table content (top 3 customers and their revenue/orders/AOV) matches both the tool output and the ground truth numerically, with only a minor unit/currency-symbol mismatch (using $ instead of €). The response is well-formatted as a table, though it adds extra narrative observations beyond the user’s request.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.6 Groundedness 10.0 Relevance 5.7 Format 9.0 Tool Calling 10.0 Instruct. 8.0 Latency 5.8 Cost 2.2 Err Rate 10.0 Overall 8.4 💬 Respuesta del agente (extracto)Here are the **Top 3 Customers by Total Revenue** from non-cancelled orders in the `benchmark-ecommerce` index:
| Rank | Customer Name | Total Revenue | Orders | Avg Order Value |
|------|------------------------|---------------|--------|-----------------|
| 1 | Hans Mueller | $68,600.24 | 55 | $1,247.28 |
| 2 | Elena Moreno Jiménez | $55,882.65 | 47 | $1,188.99 |
| 3 | Lucía Romero Gutiérrez | $52,189.00 | 43 | $1,213.70 |
**Notable observations:**
- **Hans Mueller** dominates across all metrics — highest revenue, most orders, and highest average order value. His total revenue is ~23% high
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| ✅ expert_02 🔗 | 💬 expert | expert | Compare revenue in Q3 (July-September) vs Q4 … | 51.1s | $0.2328 | 8.3 | 9.8 | 10.0 | 9.0 | 8.27 | 12 | |
❓ PreguntaCompare revenue in Q3 (July-September) vs Q4 (October-December) 2025 by product category in 'benchmark-ecommerce'. Which category grew the most between Q3 and Q4? Which declined the most?
🎯 GROUND TRUTH:
The agent should calculate revenue per category for Q3 (Jul-Sep) and Q4 (Oct-Dec) separately. Exact Q3→Q4 changes: books grew +35.9% (€6,656→€9,047, most growth), electronics declined -18.4% (€377,716→€308,321), clothing declined -10.0% (€23,738→€21,366), sports declined -32.1% (€12,095→€8,208, most decline in percentage), bags declined -25.5% (€9,194→€6,845), home was flat -0.7%. Tests temporal analysis with percentage calculations. 📋 Claim Decomposition📋 Claim Analysis
12 claims (10 central) · all grounded ●Electronics revenue was $377,716 in Q3 2025 and $308,321 in Q4 2025 (a change of -$69,395, -18.4%). ✓ Partially Supported⚓ Grounded Tool output shows electronics Q3=377,716.469... and Q4=308,321.370..., which matches the rounded values and the -18.4% change. Ground truth uses euros (€) rather than dollars ($), so the currency symbol is inconsistent even though the magnitudes/changes align. ●Home revenue was $68,742 in Q3 2025 and $68,268 in Q4 2025 (a change of -$474, -0.7%). ✓ Partially Supported⚓ Grounded Tool output shows home Q3=68,742.139... and Q4=68,268.409..., consistent with the rounded values and -0.7% change. Currency symbol differs from ground truth (€). ●Clothing revenue was $23,738 in Q3 2025 and $21,366 in Q4 2025 (a change of -$2,372, -10.0%). ✓ Partially Supported⚓ Grounded Tool output shows clothing Q3=23,737.949... and Q4=21,365.620..., matching rounding and the -10.0% change. Currency symbol differs from ground truth (€). ●Sports revenue was $12,095 in Q3 2025 and $8,208 in Q4 2025 (a change of -$3,887, -32.1%). ✓ Partially Supported⚓ Grounded Tool output shows sports Q3=12,095.000... and Q4=8,208.179..., consistent with rounding and -32.1% change. Currency symbol differs from ground truth (€). ●Bags revenue was $9,194 in Q3 2025 and $6,845 in Q4 2025 (a change of -$2,349, -25.5%). ✓ Partially Supported⚓ Grounded Tool output shows bags Q3=9,194.029... and Q4=6,844.979..., consistent with rounding and -25.5% change. Currency symbol differs from ground truth (€). ●Books revenue was $6,656 in Q3 2025 and $9,047 in Q4 2025 (a change of +$2,391, +35.9%). ✓ Partially Supported⚓ Grounded Tool output shows books Q3=6,656.100... and Q4=9,046.779..., consistent with rounding and +35.9% change. Currency symbol differs from ground truth (€). ●Books grew the most between Q3 and Q4 2025 (+35.9%). ✓ Fully Supported⚓ Grounded Ground truth states books are the biggest grower at +35.9% (6,656→9,047). Tool outputs support those values and the computed increase. ●Books was the only category to grow from Q3 to Q4 2025; the other five categories declined. ✓ Fully Supported⚓ Grounded Tool output shows books increases while electronics, home, clothing, sports, and bags all decrease; ground truth also implies only books increased. ●Electronics declined the most by absolute dollars (about $69,000) from Q3 to Q4 2025. ✓ Fully Supported⚓ Grounded From tool output, electronics drops by ~69,395, which is larger in absolute value than the declines in other categories; ground truth aligns with this change magnitude. ●Sports declined the most by percentage from Q3 to Q4 2025 (-32.1%). ✓ Fully Supported⚓ Grounded Ground truth explicitly states sports is the biggest decline in percentage (-32.1%); tool outputs support the underlying revenues (12,095→8,208). ○The books increase likely reflects holiday gifting or year-end seasonal demand. ✓ Not Verifiable⚓ Disclosed Ungrounded This is a speculative explanation not derivable from the provided tool output or ground truth; the agent presents it as a likelihood rather than a measured fact. ○Sports and bags declines suggest strong seasonality headwinds heading into winter months. ✓ Not Verifiable⚓ Disclosed Ungrounded This is an interpretive inference about causality/seasonality not supported directly by the tool outputs or ground truth; phrased as a suggestion rather than a factual measurement. 🧑⚖️ Judge ReasoningThe response correctly computes and compares Q3 vs Q4 2025 revenue by category and identifies the biggest grower (books) and biggest decliner (sports by percent; electronics by dollars), all grounded in the tool outputs and consistent with ground truth. Minor issues include using a dollar sign instead of the ground-truth euro symbol and adding speculative seasonal explanations that are not evidenced by the tool data.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.3 Groundedness 9.8 Relevance 8.3 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 3.6 Cost 1.0 Err Rate 10.0 Overall 8.3 💬 Respuesta del agente (extracto)Here's the full Q3 vs Q4 2025 revenue comparison by product category:
| Category | Q3 Revenue | Q4 Revenue | Change ($) | Change (%) |
|---|---|---|---|---|
| Electronics | $377,716 | $308,321 | -$69,395 | -18.4% |
| Home | $68,742 | $68,268 | -$474 | -0.7% |
| Clothing | $23,738 | $21,366 | -$2,372 | -10.0% |
| Sports | $12,095 | $8,208 | -$3,887 | -32.1% |
| Bags | $9,194 | $6,845 | -$2,349 | -25.5% |
| Books | $6,656 | $9,047 | +$2,391 | +35.9% |
---
**Biggest grower: Books (+35.9%)**
Books was the **only category to grow** in Q4, rising from $6,656 to $9,047 — a gain of ~$2,391. This li
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| ✅ expert_03 🔗 | 🔄 expert | expert | Show me the mapping of 'benchmark-ecommerce' … | 10.1s | $0.4392 | 9.7 | 8.1 | 10.0 | 10.0 | 8.67 | 16 4? | |
❓ Conversación (5 turns)Turn 1: Show me the mapping of 'benchmark-ecommerce' index Turn 2: What are the different order statuses and their counts? Turn 3: Now focus on the cancelled orders — what is the most common payment method among cancelled orders? Turn 4: Interesting. Now compare: is the cancellation rate higher for orders placed in the first 3 months (July-September) vs the last 3 months (October-December)? Turn 5: Summarize everything we've found in this conversation about cancellations — patterns, payment methods, timing. Be concise. 🎯 GROUND TRUTH:
The agent should: 1) Show mapping, 2) Count by status: delivered (522), shipped (123), cancelled (111), processing (97), pending (95), returned (52), 3) Most common payment method for cancelled orders is credit_card (38), followed by paypal (29) and debit_card (26), 4) Cancellation rate Q3 (Jul-Sep): 47/496 = 9.5%, Q4 (Oct-Dec): 64/504 = 12.7% — Q4 has a higher cancellation rate, 5) Provide a coherent summary. Tests deep multi-turn with 5 turns and context retention. 📋 Claim Decomposition📋 Claim Analysis
16 claims (8 central) · 4 ungrounded ●111 out of 1,000 orders were cancelled. ✓ Fully Supported⚓ Grounded Status aggregation shows 111 cancelled out of total 1000 orders. ●The overall cancellation rate is 11.1%. ✓ Fully Supported⚓ Grounded 111/1000 = 11.1%. ○Cancelled is the 3rd most common status behind Delivered and Shipped. ✓ Fully Supported⚓ Grounded Counts rank: delivered 522, shipped 123, cancelled 111, then processing 97, pending 95, returned 52. ●Credit Card leads cancellations with 38 cancelled orders (34.2%). ✓ Fully Supported⚓ Grounded Tool shows 38 cancelled orders with payment_method=credit_card; 38/111 ≈ 34.2%. ●PayPal is next with 29 cancelled orders (26.1%). ✓ Fully Supported⚓ Grounded Tool shows 29 for paypal; 29/111 ≈ 26.1%. ●Debit Card follows with 26 cancelled orders (23.4%). ✓ Fully Supported⚓ Grounded Tool shows 26 for debit_card; 26/111 ≈ 23.4%. ●Credit Card + PayPal + Debit Card account for 83.7% of all cancellations. ✓ Fully Supported⚓ Grounded 38+29+26=93; 93/111 ≈ 83.8% (rounds to 83.7%). ○These three digital/card methods account for most cancellations likely because reversals are frictionless. ✓ Not Verifiable⚓ Ungrounded [minor] The data supports the distribution but provides no evidence about the reason ("frictionless reversals"). ○Bank Transfer and Cash on Delivery have the fewest cancellations. ✓ Fully Supported⚓ Grounded Tool shows bank_transfer=10 and cash_on_delivery=8, the lowest counts among listed methods. ○Bank Transfer and Cash on Delivery having fewer cancellations suggests higher buyer commitment. ✓ Not Verifiable⚓ Ungrounded [minor] This is an interpretation not supported by any tool output. ●Cancellation rate was 9.5% in July–September vs 12.7% in October–December. ✓ Fully Supported⚓ Grounded Tool shows 47/496=9.5% and 64/504=12.7%. ○The cancellation rate increased by about 34% relatively from Jul–Sep to Oct–Dec. ✓ Fully Supported⚓ Grounded (12.7-9.5)/9.5 ≈ 33.7%, which is ~34%. ○Oct–Dec saw more returns and fewer delivered orders than Jul–Sep. ✓ Fully Supported⚓ Grounded Returns: 30 vs 22; Delivered: 248 vs 274. ○The Oct–Dec pattern suggests broader fulfillment or satisfaction issues potentially linked to holiday season pressures. ✓ Not Verifiable⚓ Ungrounded [minor] The tool output shows counts only; it does not evidence fulfillment/satisfaction issues or holiday pressure causality. ●Cancellations are disproportionately driven by easy-to-reverse payment methods and worsen in Q4. ✓ Partially Supported⚓ Partially Grounded Worsen in Q4 is supported by higher Oct–Dec rate; "driven by easy-to-reverse" is causal/speculative and not grounded. … y 1 más 🧑⚖️ Judge ReasoningThe final response correctly retains and summarizes key quantitative findings across turns (overall cancellations, payment-method breakdown, and Jul–Sep vs Oct–Dec cancellation rates), matching the tool outputs and ground truth. However, it adds several causal interpretations (e.g., frictionless reversals, buyer commitment, holiday/fulfillment issues) that are not grounded in the data, reducing evidentiary quality but not materially harming context retention.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 9.7 Groundedness 8.1 Relevance 5.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 8.5 Cost 1.0 Err Rate 10.0 Overall 8.7 💬 Respuesta del agente (extracto)Here's a concise summary of everything uncovered about cancellations in the `benchmark-ecommerce` index:
---
### Cancellation Overview
- **111 out of 1,000 orders** were cancelled — an **11.1% overall cancellation rate**, making it the 3rd most common status behind Delivered and Shipped.
---
### Payment Methods
- **Credit Card** leads cancellations (38 orders, 34.2%), followed by PayPal (26.1%) and Debit Card (23.4%).
- These three digital/card methods account for **83.7%** of all cancellations — likely because reversals are frictionless.
- **Bank Transfer** and **Cash on Delivery** have t
| ||||||||||||
| ✅ format_01 🔗 | 💬 hard | format_strict | Give me exactly 3 cancelled orders from 'benc… | 23.8s | $0.2184 | 6.5 | 7.8 | 10.0 | 7.0 | 7.56 | 6 1✗ | |
❓ PreguntaGive me exactly 3 cancelled orders from 'benchmark-ecommerce' as a JSON array. ONLY output the JSON array, nothing else. No markdown, no explanation, no code blocks. Each object should have order_id, customer name, and total_amount.
🎯 GROUND TRUTH:
The agent should search for cancelled orders, pick 3, and output ONLY a JSON array with the specified fields. No markdown formatting, no explanation text. Tests strict format adherence. 📋 Claim Decomposition📋 Claim Analysis
6 claims (6 central) · 1 contradicted · all grounded ●The response provides exactly three orders in a single JSON array. ✓ Fully Supported⚓ Disclosed Ungrounded Ground truth requires exactly 3 orders and only a JSON array; the response is a JSON array with 3 objects. This is a formatting/structure property and does not need tool support. ●Each order object contains the fields order_id, customer name, and total_amount as requested. ✓ Contradicted⚓ Grounded [major] The response uses the key "customer_name" rather than the requested "customer name" (as written in the user request/ground truth expectation), so it does not match the specified field requirement. This is directly observable from the agent response itself. ●Order ORD-2025-00014 belongs to customer John Smith and has total_amount 2841.48. ✓ Not Verifiable⚓ Grounded Ground truth does not specify which exact orders/amounts must be returned, only that they should be cancelled orders. The tool output explicitly includes ORD-2025-00014 with customer.name John Smith and total_amount 2841.47998046875, which matches the agent’s 2841.48 by rounding. ●Order ORD-2025-00020 belongs to customer Pedro Sánchez Díaz and has total_amount 899.71. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not specify which exact orders/amounts must be returned. The tool output shows a cancelled row for ORD-2025-00020 with customer.name Pedro Sánchez Díaz, but the displayed tool output is truncated before clearly showing the corresponding total_amount value, so the specific amount cannot be fully confirmed from what is visible. ●Order ORD-2025-00022 belongs to customer Pedro Sánchez Díaz and has total_amount 321.24. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not specify which exact orders/amounts must be returned. The tool output is explicitly truncated and may contain ORD-2025-00022, but ORD-2025-00022 and its total_amount are not visible in the provided snippet, so support is incomplete but not treated as hallucinated per the truncation rule. ●All three listed orders are cancelled orders from the benchmark-ecommerce index. ✓ Not Verifiable⚓ Partially Grounded The tool query shown filters WHERE status == "cancelled", and the visible rows for ORD-2025-00014 and ORD-2025-00020 have status "cancelled". ORD-2025-00022 is not visible due to truncation, so the 'all three' portion cannot be fully confirmed from the snippet. 🧑⚖️ Judge ReasoningThe agent complied with the strict output container requirement by returning only a JSON array with exactly three objects and no extra text. However, it likely failed the requested field naming by using "customer_name" instead of "customer name" as specified. Two of the three orders are clearly supported by the tool output (with one amount not fully visible due to truncation), while the third cannot be directly verified from the truncated tool snippet.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 6.5 Groundedness 7.8 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 7.0 Latency 6.1 Cost 1.0 Err Rate 10.0 Overall 7.6 💬 Respuesta del agente (extracto)[{"order_id":"ORD-2025-00014","customer_name":"John Smith","total_amount":2841.48},{"order_id":"ORD-2025-00020","customer_name":"Pedro Sánchez Díaz","total_amount":899.71},{"order_id":"ORD-2025-00022","customer_name":"Pedro Sánchez Díaz","total_amount":321.24}]
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| ✅ format_02 🔗 | 💬 medium | format_strict | Answer with ONLY a number, nothing else: How … | 24.9s | $0.3022 | 10.0 | 10.0 | 10.0 | 10.0 | 9.15 | 1 | |
❓ PreguntaAnswer with ONLY a number, nothing else: How many orders in 'benchmark-ecommerce' have status 'pending'?
🎯 GROUND TRUTH:
The exact answer is 95. The agent should respond with ONLY the number '95' — no words, no explanation, no formatting. Tests extreme instruction following. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●The number of orders in 'benchmark-ecommerce' with status 'pending' is 95. ✓ Fully Supported⚓ Grounded Matches the ground truth exact expected answer (95) and is directly supported by the tool output showing COUNT(*) = 95 for status == "pending". 🧑⚖️ Judge ReasoningThe agent returned the exact correct count (95), matching the ground truth precisely. The value is also directly grounded in the tool output count aggregation, and the agent followed the instruction to respond with only a number.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 95FOUND: 95📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 6.0 Cost 1.0 Err Rate 10.0 Overall 9.2 💬 Respuesta del agente (extracto)95
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| ✅ instruction_01 🔗 | 💬 medium | instruction_following | List all unique product categories in the 'be… | 26.0s | $0.1143 | 10.0 | 9.8 | 10.0 | 10.0 | 9.09 | 5 | |
❓ PreguntaList all unique product categories in the 'benchmark-ecommerce' index. Format your response as a markdown table with columns: Category, Description (infer from category name).
🎯 GROUND TRUTH:
The agent should find the 6 unique product categories: electronics (413 orders), home (203), clothing (186), books (104), bags (52), sports (42) — and present them in a markdown table with columns Category and Description. Tests instruction following for specific output format. 📋 Claim Decomposition📋 Claim Analysis
5 claims (4 central) · all grounded ●There are 6 unique product categories in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Ground truth specifies 6 unique categories, and the tool output lists 6 distinct `product.category` values. ●The 6 unique categories are: bags, books, clothing, electronics, home, and sports. ✓ Fully Supported⚓ Grounded These exact six category values appear in the tool output tabular results and match the ground truth list. ●These categories were retrieved directly from the `product.category` keyword field in the index. ✓ Fully Supported⚓ Grounded Index mapping shows `product.category` is a `keyword`, and the ES|QL query groups by `product.category`, supporting the retrieval source. ●The response provides a markdown table with columns: Category and Description. ✓ Fully Supported⚓ Grounded The agent output is a markdown table with exactly the requested column headers. ○The descriptions in the table are inferred from the category names (i.e., not directly sourced from the index data). ✓ Fully Supported⚓ Disclosed Ungrounded The user asked to infer descriptions; the agent explicitly notes descriptions are inferred, and there is no tool output providing descriptions. 🧑⚖️ Judge ReasoningThe agent correctly identifies all 6 unique product categories and presents them in the requested markdown table format. Category values are fully supported by the tool output, while the descriptions are appropriately disclosed as inferred (and thus not grounded in tool data).
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 9.8 Relevance 8.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.9 Cost 2.7 Err Rate 10.0 Overall 9.1 💬 Respuesta del agente (extracto)Here are all **6 unique product categories** found in the `benchmark-ecommerce` index:
| Category | Description |
|---|---|
| **Bags** | Handbags, backpacks, luggage, and other carrying accessories for everyday use or travel. |
| **Books** | Printed or digital reading materials including fiction, non-fiction, educational, and reference titles. |
| **Clothing** | Apparel and wearable items such as shirts, pants, dresses, outerwear, and fashion accessories. |
| **Electronics** | Electronic devices and gadgets including smartphones, laptops, audio equipment, and accessories. |
| **Home** | Home
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| ✅ instruction_02 🔗 | 💬 medium | instruction_following | In exactly 3 bullet points, summarize what th… | 29.5s | $0.0723 | 0.0 | 8.6 | 10.0 | 4.0 | 5.78 | 9 1✗ 1? | |
❓ PreguntaIn exactly 3 bullet points, summarize what the 'benchmark-ecommerce' index contains. Use the mapping and a quick search to understand the data.
🎯 GROUND TRUTH:
The agent should examine the mapping and/or data, then respond with exactly 3 bullet points summarizing the ecommerce orders data. The index contains 1000 e-commerce orders from 20 customers across 6 product categories (electronics, home, clothing, books, bags, sports) with 6 statuses (delivered, shipped, cancelled, processing, pending, returned). Tests adherence to response format constraints — must be exactly 3 bullet points. 📋 Claim Decomposition📋 Claim Analysis
9 claims (7 central) · 1 contradicted · 1 ungrounded ●Each document in the index represents a single e-commerce order. ✓ Fully Supported⚓ Partially Grounded Ground truth describes the index as containing e-commerce orders, supporting the idea of order-level documents. The mapping shows order-oriented fields (order_id, order_date, status, product, customer), but tool output does not explicitly confirm the document granularity. ●Documents capture customer details including name, email, city, and country. ✓ Not Verifiable⚓ Grounded Ground truth does not explicitly list customer fields, so correctness cannot be verified against it. The mapping clearly includes customer.name, customer.email, customer.city, and customer.country. ●Documents include order metadata such as order ID, order date, priority, status, and flags like is_gift, plus free-text notes. ✓ Not Verifiable⚓ Grounded Ground truth does not enumerate these fields, so correctness vs ground truth is not directly verifiable. The mapping contains order_id, order_date, priority, status, is_gift, and notes. ●Documents include product information: product name, category, and subcategory. ✓ Partially Supported⚓ Grounded Ground truth states the index spans product categories, which is consistent with having a product.category field; it does not mention name/subcategory. The mapping includes product.name, product.category, and product.subcategory. ●Documents include transaction financials such as unit_price, quantity, total_amount, tax_amount, discount_pct, and shipping_cost. ✓ Not Verifiable⚓ Grounded Ground truth does not specify these financial fields, so correctness cannot be confirmed against it. The mapping lists unit_price, quantity, total_amount, tax_amount, discount_pct, and shipping_cost. ●Each record covers delivery lifecycle data including estimated_delivery and delivered_at, as well as shipping_method and payment_method. ✓ Not Verifiable⚓ Grounded Ground truth does not explicitly mention these lifecycle fields. The mapping includes estimated_delivery, delivered_at, shipping_method, and payment_method. ○Documents include post-purchase signals like rating and tags for analyzing satisfaction trends. ✓ Not Verifiable⚓ Grounded Ground truth does not mention ratings/tags. The mapping includes rating and tags; the analysis use-case is interpretive but consistent with those fields. ●The index is currently empty. ✓ Contradicted⚓ Ungrounded [critical] Ground truth explicitly says the index contains 1000 e-commerce orders, contradicting emptiness. Tool output shows a mapping but provides no evidence the index is empty; the blank search outputs do not establish emptiness (they may reflect query mismatch or omitted results). ○The summary is based on the index mapping schema alone. ✓ Fully Supported⚓ Grounded Given the tool output includes only the mapping (and no actual hits returned from search), the response is evidently derived from the mapping. 🧑⚖️ Judge ReasoningThe response largely summarizes fields that are strongly supported by the mapping, so most schema-related claims are grounded. However, it fails the key ground-truth expectations about dataset contents (e.g., 1000 orders, categories/statuses) and introduces a critical incorrect claim that the index is empty. It also violates the 'exactly 3 bullet points' constraint by adding an extra note outside the bullets.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 8.6 Relevance 7.8 Format 6.0 Tool Calling 10.0 Instruct. 4.0 Latency 5.6 Cost 4.4 Err Rate 10.0 Overall 5.8 💬 Respuesta del agente (extracto)- **Customer & Order Records** — Each document represents a single e-commerce order, capturing customer details (name, email, city, country), order metadata (order ID, date, priority, status), and flags like `is_gift` and free-text `notes`.
- **Product & Financial Data** — Documents include product information (name, category, subcategory) alongside full transaction financials: unit price, quantity, total amount, tax, discount percentage, and shipping cost.
- **Fulfillment & Satisfaction Tracking** — Each record covers the delivery lifecycle (estimated and actual delivery dates, shipping met
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| ✅ multiturn_01 🔗 | 🔄 hard | multi_turn | Show me the mapping of the 'benchmark-ecommer… | 17.0s | $0.2556 | 9.3 | 8.3 | 10.0 | 10.0 | 8.54 | 6 1? | |
❓ Conversación (3 turns)Turn 1: Show me the mapping of the 'benchmark-ecommerce' index Turn 2: Based on that mapping, how many unique product categories are there? Use a query to find out. Turn 3: Now show me the total revenue for the top category you found. 🎯 GROUND TRUTH:
The agent should: 1) Show the mapping (fields include product.category, total_amount, etc.), 2) Find there are exactly 6 unique product categories (electronics, home, clothing, books, bags, sports), 3) Remember that electronics is the top category and calculate its total revenue: €686,037.84 from 413 orders. Tests context retention across 3 turns. 📋 Claim Decomposition📋 Claim Analysis
6 claims (4 central) · 1 ungrounded ●The total revenue for the Electronics category is $686,037.84. ✓ Partially Supported⚓ Partially Grounded Tool output supports the numeric total_revenue ≈ 686,037.84 for category "electronics", but the agent states the currency as "$" whereas the expected behavior/ground truth indicates €. ●This total revenue was calculated using the shown ES|QL query. ✓ Fully Supported⚓ Grounded Tool output includes the exact ES|QL query used to compute SUM(total_amount) for product.category == "electronics". ●The query filters to records where `product.category` == "electronics". ✓ Fully Supported⚓ Grounded The tool-captured query explicitly contains WHERE `product.category` == "electronics". ●The query computes total revenue as SUM(total_amount). ✓ Fully Supported⚓ Grounded The tool output shows `STATS total_revenue = SUM(total_amount)` and returns a numeric result. ○Electronics leads in order count with 413 orders. ✓ Fully Supported⚓ Grounded The earlier tool output table shows electronics has order_count = 413, which is the highest among categories. ○Electronics is clearly the dominant category in this dataset — both by volume and revenue. ✓ Partially Supported⚓ Ungrounded [minor] Dominance by volume is supported by the 413 order_count being the highest, but dominance by revenue is not established because the tool output only provides electronics revenue and does not compare revenue across categories. 🧑⚖️ Judge ReasoningThe final answer correctly retains that electronics is the top category and uses the appropriate ES|QL to compute its revenue, matching the tool-derived total. The main issue is an unsupported currency symbol ($ vs expected €) and an overreach in claiming revenue dominance without comparative revenue evidence.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 9.3 Groundedness 8.3 Relevance 6.7 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 6.8 Cost 1.0 Err Rate 10.0 Overall 8.5 💬 Respuesta del agente (extracto)The total revenue for the **Electronics** category is **$686,037.84**, calculated using this query:
```esql
FROM benchmark-ecommerce
| WHERE `product.category` == "electronics"
| STATS total_revenue = SUM(total_amount)
```
Given that Electronics also leads in order count (413 orders), it is clearly the dominant category in this dataset — both by volume and revenue.
| ||||||||||||
| ✅ multiturn_02 🔗 | 🔄 hard | multi_turn | Search for orders in the 'benchmark-ecommerce… | 31.0s | $0.3662 | 9.8 | 8.2 | 10.0 | 9.0 | 8.41 | 12 2? | |
❓ Conversación (2 turns)Turn 1: Search for orders in the 'benchmark-ecommerce' index from customers in Spain Turn 2: Now narrow it down: from those Spanish customers, show me only the cancelled orders with high priority 🎯 GROUND TRUTH:
The agent should first search for Spanish customers (736 orders from Spain), then refine the search adding status=cancelled and priority=high (exactly 15 orders match all three conditions: country=Spain, status=cancelled, priority=high). Tests progressive query refinement across 2 turns. 📋 Claim Decomposition📋 Claim Analysis
12 claims (8 central) · 2 ungrounded ●There are 15 cancelled, high-priority orders from Spanish customers. ✓ Fully Supported⚓ Partially Grounded Ground truth states exactly 15 orders match (country=Spain AND status=cancelled AND priority=high). Tool output shows this exact filtered query and multiple matching rows, but the returned values list is truncated so the full count cannot be independently confirmed from the visible tool rows. ●The results are sorted by most recent date (descending). ✓ Fully Supported⚓ Grounded Tool query explicitly uses `SORT order_date DESC`, and visible rows follow descending dates (e.g., Dec 27 then Dec 22 then Dec 20). ●Order ORD-2025-00037 (Dec 27, 2025) for María García López in Madrid is cancelled/high priority and is for 'Cafetera Automática Deluxe' totaling €1,059.61. ✓ Fully Supported⚓ Grounded Directly matches tool row: order_id ORD-2025-00037, 2025-12-27T08:35Z, customer María García López (Madrid), priority high, status cancelled, product Cafetera Automática Deluxe, total_amount 1059.609985... (rounds to €1,059.61). ●Order ORD-2025-00433 (Dec 22, 2025) for Carlos Rodríguez Martín in Barcelona is cancelled/high priority and is for 'Mechanical Keyboard RGB' totaling €706.36. ✓ Fully Supported⚓ Grounded Directly matches tool row: ORD-2025-00433, 2025-12-22T22:35Z, Carlos Rodríguez Martín (Barcelona), high/cancelled, Mechanical Keyboard RGB, total_amount 706.359985... (rounds to €706.36). ●Order ORD-2025-00426 (Dec 20, 2025) for Sofia Torres Navarro in Alicante is cancelled/high priority and is for 'Reloj Fitness Tracker' totaling €629.44. ✓ Fully Supported⚓ Grounded Directly matches tool row: ORD-2025-00426, 2025-12-20T20:47Z, Sofia Torres Navarro (Alicante), high/cancelled, Reloj Fitness Tracker, total_amount 629.440002... (€629.44). ●Order ORD-2025-00408 (Dec 6, 2025) for Andrés Gómez Castro in Córdoba is cancelled/high priority and is for 'Reloj Fitness Tracker' totaling €182.54. ✓ Fully Supported⚓ Grounded Directly matches tool row: ORD-2025-00408, 2025-12-06T12:19Z, Andrés Gómez Castro (Córdoba), high/cancelled, Reloj Fitness Tracker, total_amount 182.539993... (€182.54). ●Order ORD-2025-00513 (Nov 18, 2025) for Javier Ruiz Hernández in Murcia is cancelled/high priority and is for 'Smartphone X12' totaling €3,602.81. ✓ Fully Supported⚓ Grounded Directly matches tool row: ORD-2025-00513, 2025-11-18T12:21Z, Javier Ruiz Hernández (Murcia), high/cancelled, Smartphone X12, total_amount 3602.810058... (€3,602.81). ○The highest-value cancellation is €3,602.81 by Javier Ruiz Hernández (Murcia) for Smartphone X12. ✓ Not Verifiable⚓ Partially Grounded The tool output confirms an order with €3,602.81 for that customer/product, but because the tool results list is truncated, it cannot be verified that no other of the 15 orders has a higher total. ○The most cancelled products among these orders are Mechanical Keyboard RGB (3×) and Reloj Fitness Tracker (3×). ✓ Not Verifiable⚓ Ungrounded [minor] Counts across all 15 require the full set of rows; the tool output is truncated and does not provide enough visible evidence to verify the 3×/3× product frequency claim. ○Repeat cancellers include: Javier Ruiz Hernández (2 orders), Miguel Hernández Romero (2), Carlos Rodríguez Martín (2), and Ana Fernández Ruiz (2). ✓ Not Verifiable⚓ Ungrounded [minor] The tool output is truncated; without all 15 rows visible, the repeated-customer counts cannot be confirmed from the provided tool data. ○The date range of these 15 orders is August through December 2025. ✓ Not Verifiable⚓ Partially Grounded Visible tool rows include dates in November/December 2025; however, because the tool output is truncated, the earliest date (August) cannot be verified from the visible portion. ●These 15 orders represent the full set and no results were cut off. ✓ Fully Supported⚓ Partially Grounded Ground truth states exactly 15 orders match the refined conditions. The tool query uses LIMIT 100 (so truncation would not be due to query limiting), but the provided tool output text is truncated, preventing full confirmation purely from the displayed rows. 🧑⚖️ Judge ReasoningThe agent correctly performed progressive refinement from country=Spain to adding status=cancelled and priority=high, matching the expected 15 results. The specific order rows shown at the top are well-supported by the tool output, while several derived “Notable Patterns” (counts and max comparisons) are not fully verifiable due to truncated tool data and thus are not grounded.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.8 Groundedness 8.2 Relevance 6.7 Format 10.0 Tool Calling 10.0 Instruct. 9.0 Latency 5.4 Cost 1.0 Err Rate 10.0 Overall 8.4 💬 Respuesta del agente (extracto)Here are all **15 cancelled, high-priority orders from Spanish customers**, sorted by most recent date:
| Order ID | Date | Customer | City | Product | Total |
|---|---|---|---|---|---|
| ORD-2025-00037 | Dec 27, 2025 | María García López | Madrid | Cafetera Automática Deluxe | €1,059.61 |
| ORD-2025-00433 | Dec 22, 2025 | Carlos Rodríguez Martín | Barcelona | Mechanical Keyboard RGB | €706.36 |
| ORD-2025-00426 | Dec 20, 2025 | Sofia Torres Navarro | Alicante | Reloj Fitness Tracker | €629.44 |
| ORD-2025-00408 | Dec 6, 2025 | Andrés Gómez Castro | Córdoba | Reloj Fitness Tracker | €182.54 |
| ||||||||||||
| ✅ multiturn_03 🔗 | 🔄 medium | multi_turn | Count the orders in the 'benchmark-ecomerce' … | 4.8s | $0.1501 | 10.0 | 10.0 | 10.0 | 10.0 | 9.02 | 2 | |
❓ Conversación (2 turns)Turn 1: Count the orders in the 'benchmark-ecomerce' index Turn 2: Sorry, I misspelled it. The correct index name is 'benchmark-ecommerce' (with double m). Try again. 🎯 GROUND TRUTH:
The agent should handle the user's typo correction gracefully. First turn may error, second turn should use the corrected name and return count of 1000. Tests error recovery. 📋 Claim Decomposition📋 Claim Analysis
2 claims (1 central) · all grounded ○The count was already retrieved from the correct index. ✓ Fully Supported⚓ Grounded Tool logs show the agent ultimately queried `benchmark-ecommerce` and obtained the count (1000) before producing the final response, so stating the count was already retrieved is supported. ●There are 1,000 orders in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded The ES|QL tool output shows `STATS total_orders = COUNT(*)` returned `1000` for `benchmark-ecommerce`. 🧑⚖️ Judge ReasoningThe final response’s key factual statement (1000 orders in `benchmark-ecommerce`) is directly supported by the ES|QL count result in the tool output. The agent also correctly handled the user’s typo correction and maintained continuity by using the corrected index name.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.list_indices
USED: platform.core.search platform.core.list_indices
🎯 Exact Answer Check ✅EXPECTED: 1000FOUND: 1000📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 5.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 10.0 Cost 1.4 Err Rate 7.0 Overall 9.0 💬 Respuesta del agente (extracto)No worries! The count was already retrieved from the correct index.
There are **1,000 orders** in the `benchmark-ecommerce` index.
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| ✅ reasoning_01 🔗 | 💬 hard | reasoning | First, show me the mapping of the 'benchmark-… | 34.5s | $0.1371 | 9.5 | 9.0 | 10.0 | 8.0 | 8.43 | 7 1? | |
❓ PreguntaFirst, show me the mapping of the 'benchmark-ecommerce' index. Then, based on the mapping, write a query to find the top 3 customers by number of orders.
🎯 GROUND TRUTH:
The agent should first get the mapping to understand the schema, then query to group by customer.name.keyword, count orders, and sort descending limited to 3. The top 3 customers by order count are: Hans Mueller (59 orders), John Smith (59 orders), and Isabel Jiménez Ortega (58 orders). This requires multi-step reasoning using mapping info. 📋 Claim Decomposition📋 Claim Analysis
7 claims (5 central) · 1 ungrounded ○The `benchmark-ecommerce` index models e-commerce orders with one document per order. ✓ Fully Supported⚓ Ungrounded [minor] Ground truth states the assumption that each document represents one order, so the claim is correct, but the tool outputs only show mappings/queries/results and do not explicitly confirm the one-document-per-order modeling. ●The mapping includes order fields such as `order_id` (keyword); `order_date`, `delivered_at`, `estimated_delivery` (date); `status`, `priority`, `payment_method`, `shipping_method` (keyword); multiple float fields (e.g., `total_amount`, `tax_amount`); integers `quantity` and `rating`; `is_gift` (boolean); `notes` (text); and `tags` (keyword). ✓ Fully Supported⚓ Grounded These fields and types match the mapping returned by `platform.core.get_index_mapping`. ●Customer fields under `customer.*` include `customer.name` as `text` with a `keyword` multi-field, and `customer.email`, `customer.city`, `customer.country` as `keyword`. ✓ Fully Supported⚓ Grounded This exactly matches the mapping output (customer.name has `fields.keyword`, and the other customer fields are `keyword`). ○Product fields under `product.*` include `product.name` as `text` with a `keyword` multi-field, and `product.category` and `product.subcategory` as `keyword`. ✓ Fully Supported⚓ Grounded The mapping output shows these product fields and types. ●To find the top 3 customers by number of orders, the agent uses an ES|QL query that groups by `customer.email`, computes `order_count = COUNT(order_id)`, captures `customer_name = VALUES(customer.name)`, sorts by `order_count` descending, and limits to 3. ✓ Partially Supported⚓ Grounded This query is exactly what the tool output shows and is a valid way to get the top customers; however, the ground truth expectation specifically mentions grouping by `customer.name.keyword` (the agent grouped by email instead). ●The top 3 customers by order count are John Smith (59), Hans Mueller (59), and Isabel Jiménez Ortega (58). ✓ Fully Supported⚓ Grounded These names and counts match both the ground truth and the tool’s tabular results. ●John Smith and Hans Mueller are tied for the highest order count with 59 orders each, followed by Isabel Jiménez Ortega with 58. ✓ Fully Supported⚓ Grounded The tool results show the tie at 59 and the next value at 58; this also matches the ground truth. 🧑⚖️ Judge ReasoningThe response accurately summarizes the index mapping and provides a working ES|QL aggregation that matches the tool output and returns the correct top-3 customers and counts. The main deviation from the expected approach is grouping by `customer.email` rather than `customer.name.keyword`, and the response summarizes the mapping instead of printing the raw mapping JSON.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 9.5 Groundedness 9.0 Relevance 7.1 Format 9.0 Tool Calling 10.0 Instruct. 8.0 Latency 5.0 Cost 1.9 Err Rate 10.0 Overall 8.4 💬 Respuesta del agente (extracto)## Part 1: Mapping — `benchmark-ecommerce`
The index models e-commerce orders, one document per order, with the following fields:
**Order fields**
- `order_id` — `keyword`
- `order_date`, `delivered_at`, `estimated_delivery` — `date`
- `status`, `priority`, `payment_method`, `shipping_method` — `keyword`
- `shipping_cost`, `unit_price`, `total_amount`, `tax_amount`, `discount_pct` — `float`
- `quantity`, `rating` — `integer`
- `is_gift` — `boolean`
- `notes` — `text`
- `tags` — `keyword`
**Customer fields (nested under `customer.*`)**
- `customer.name` — `text` + `keyword` (multi-field)
- `
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| ✅ reasoning_02 🔗 | 💬 medium | reasoning | Analyze the 'benchmark-ecommerce' index: how … | 27.0s | $0.1039 | 9.9 | 9.0 | 10.0 | 10.0 | 8.66 | 10 | |
❓ PreguntaAnalyze the 'benchmark-ecommerce' index: how many orders are there per status? Which status has the most orders? Give me a summary.
🎯 GROUND TRUTH:
The agent should group by status and count orders. The exact distribution is: delivered (522), shipped (123), cancelled (111), processing (97), pending (95), returned (52). 'delivered' has the most orders with 522. The agent should provide a clear summary interpreting these results. 📋 Claim Decomposition📋 Claim Analysis
10 claims (3 central) · all grounded ●Orders by status in `benchmark-ecommerce`: delivered 522, shipped 123, cancelled 111, processing 97, pending 95, returned 52. ✓ Fully Supported⚓ Grounded These exact counts match the Ground Truth distribution and are directly present in the tool output table. ●The index contains 1,000 total orders. ✓ Fully Supported⚓ Partially Grounded The total (522+123+111+97+95+52) sums to 1,000, matching Ground Truth; however, the tool output does not explicitly state the total—it's derived from the grouped counts. ●Delivered has the most orders (522). ✓ Fully Supported⚓ Grounded Ground Truth and tool output both show delivered at 522 and sorted highest. ○Delivered accounts for ~52.2% of all orders. ✓ Fully Supported⚓ Partially Grounded 52.2% is consistent with 522/1000; tool output provides counts but not percentages, so this is a correct derived calculation. ○Shipped accounts for ~12.3%, cancelled ~11.1%, processing ~9.7%, pending ~9.5%, and returned ~5.2% of all orders. ✓ Fully Supported⚓ Partially Grounded All percentages correctly correspond to each count divided by the derived total of 1,000; tool output contains the counts but not the computed shares. ○Orders currently in motion (shipped + processing + pending) total 315 orders (~31.5%). ✓ Fully Supported⚓ Partially Grounded 315 and 31.5% correctly derive from tool counts (123+97+95) and total 1,000, but the tool output does not explicitly provide this combined metric. ○Cancelled orders (111) represent ~11.1% of all orders. ✓ Fully Supported⚓ Partially Grounded The count (111) is grounded; the percentage is a correct derivation from counts/total but not explicitly in the tool output. ○Cancelled plus returned orders total ~16.3% of all orders. ✓ Fully Supported⚓ Partially Grounded The combined count (111+52=163) and percent (16.3% of 1,000) are correctly derived from the tool counts; the combined metric itself is not directly shown in tool output. ○The delivered share is a strong indicator of successful order fulfillment. ✓ Not Verifiable⚓ Disclosed Ungrounded This is an interpretive/business judgment not determinable from the provided Ground Truth or tool output; it is presented as commentary rather than a data-backed fact. ○A 5.2% return rate is relatively healthy for an e-commerce operation. ✓ Not Verifiable⚓ Disclosed Ungrounded While 5.2% is a correct derived percentage, whether it is 'healthy' is normative and not supported by any benchmark data in the tool output or Ground Truth. 🧑⚖️ Judge ReasoningThe response correctly reports the per-status order counts and correctly identifies `delivered` as the highest, matching both Ground Truth and tool output. Additional totals/percentages and combined-category metrics are accurate derivations from the tool-provided counts, though not explicitly present in the tool output. A few qualitative judgments (e.g., 'healthy' return rate) are not verifiable from the provided data but are framed as commentary rather than as hard facts.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.9 Groundedness 9.0 Relevance 3.0 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.8 Cost 3.1 Err Rate 10.0 Overall 8.7 💬 Respuesta del agente (extracto)Here's a full breakdown of orders by status in the **`benchmark-ecommerce`** index:
| Status | Order Count | Share |
|---|---|---|
| **Delivered** | 522 | ~52.2% |
| Shipped | 123 | ~12.3% |
| Cancelled | 111 | ~11.1% |
| Processing | 97 | ~9.7% |
| Pending | 95 | ~9.5% |
| Returned | 52 | ~5.2% |
| **Total** | **1,000** | **100%** |
---
**Key Takeaways:**
- **`delivered` is the top status** — with 522 orders, it accounts for over half of all orders (52.2%), which is a strong indicator of successful order fulfillment.
- **Active pipeline is healthy** — orders currently in motion (`shipped`
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| ✅ search_01 🔗 | 💬 easy | tool_usage | List all indices in my Elasticsearch cluster | 49.5s | $0.1916 | 8.2 | 7.1 | 10.0 | 6.0 | 7.17 | 8 1✗ 3? | |
❓ PreguntaList all indices in my Elasticsearch cluster
🎯 GROUND TRUTH:
The agent should use the list_indices tool and return a list of indices in the cluster. The list must include 'benchmark-ecommerce' (1000 order documents) and 'benchmark-customers' (20 customer documents). The cluster contains approximately 47+ indices in total. 📋 Claim Decomposition📋 Claim Analysis
8 claims (3 central) · 1 contradicted · 3 ungrounded ●The cluster has 46 regular indices. ✓ Partially Supported⚓ Grounded Tool output lists 46 index names (supports the '46' count), but the provided ground truth expects approximately 47+ indices in total, so the count is likely incomplete relative to the expectation. ●The cluster contains the indices 'benchmark-customers' and 'benchmark-ecommerce'. ✓ Fully Supported⚓ Grounded Both indices are explicitly required by the ground truth and both appear in the tool output index list. ●The regular indices in the cluster include: backup, borrado, borrado_pipeline, busqueda_semantica, byviz_test_indexacion, content-notion-dd4f, dataset, dataset_vectores, elastic_canvas, estados, estados_1, indice_1, indice_2, indice_completo, kb_notion, llm-benchmark-results, llm-benchmark-summary, logs-cloud_security_posture.scores-default, logs-cloud_security_posture.vulnerabilities_latest-default, logs-tenant, logs-ti_abusech_latest.dest_ja3_fingerprints-3, logs-ti_abusech_latest.dest_malware-3, logs-ti_abusech_latest.dest_malwarebazaar-3, logs-ti_abusech_latest.dest_sslblacklist-3, logs-ti_abusech_latest.dest_threatfox-3, logs-ti_abusech_latest.dest_url-3, metrics-endpoint.metadata_current_default, ml_beaconing-1.3.0, my-index-inference, my-index-inference-e5, partial-restored-.ds-indice_frozen-2026.02.12-000002, pedido_clientes, pedidos, pipeline1, pipeline1-borrado, pipeline1-reindex, prueba2-000005, prueba_pdf_1, risk-score.risk-score-latest-default, security_solution-aws.misconfiguration_latest-v2, security_solution-awsconfig.misconfiguration_latest-v1, security_solution-awsinspector.vulnerability_latest-v1, security_solution-cloud_security_posture.misconfiguration_latest-v1, slowlog_iam. ✓ Not Verifiable⚓ Grounded The ground truth only mandates inclusion of certain benchmark indices and an approximate total count, not the full exact name list; however, every name listed here is present in the tool output indices array. ○There are 27 aliases in the cluster. ✓ Not Verifiable⚓ Partially Grounded The tool output includes an 'aliases' section but it is truncated, so the exact alias count cannot be confirmed from the visible tool output; the ground truth does not specify alias counts. ○The alias 'logs-ti_abusech_latest.malware' points to the index 'logs-ti_abusech_latest.dest_malware-3'. ✓ Not Verifiable⚓ Partially Grounded This mapping is plausible and could be present in the truncated alias output, but it is not visible in the provided tool snippet; the ground truth does not address aliases. ○The cluster has 75+ data streams. ✓ Not Verifiable⚓ Ungrounded [major] The provided tool output shows indices and aliases; it does not show any data stream list or data stream count, so '75+' is not supported by tool output, and the ground truth does not mention data streams. ○Data streams include APM and Elastic Agent streams such as 'logs-apm.app.python_demo_backend-default' and 'logs-elastic_agent-default'. ✓ Not Verifiable⚓ Ungrounded [major] No data stream names are present in the tool output provided; the agent’s listed examples are therefore unsupported by the tool output and not required/confirmed by the ground truth. ○The response provides a full overview of all resources in the Elasticsearch cluster. ✓ Contradicted⚓ Ungrounded [major] Ground truth expects listing all indices; the agent additionally claims completeness over 'all resources' and introduces data streams without tool support. The response is also visibly cut off mid-table, further undermining the 'full overview' claim. 🧑⚖️ Judge ReasoningThe agent correctly used the tool-derived index names and included the required benchmark indices, but its stated index count (46) likely falls short of the ground truth expectation of ~47+ indices. It also added aliases and especially data stream details that are not supported by the provided tool output and goes beyond the user’s request to list indices; the response appears truncated, which harms completeness and presentation.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.list_indices
USED: platform.core.list_indices
📊 Todas las puntuacionesCorrectness 8.2 Groundedness 7.1 Relevance 3.8 Format 7.0 Tool Calling 10.0 Instruct. 6.0 Latency 3.7 Cost 1.0 Err Rate 10.0 Overall 7.2 💬 Respuesta del agente (extracto)Here is a full overview of all resources in your Elasticsearch cluster:
---
## Regular Indices (46)
- `backup`
- `benchmark-customers`
- `benchmark-ecommerce`
- `borrado`
- `borrado_pipeline`
- `busqueda_semantica`
- `byviz_test_indexacion`
- `content-notion-dd4f`
- `dataset`
- `dataset_vectores`
- `elastic_canvas`
- `estados`
- `estados_1`
- `indice_1`
- `indice_2`
- `indice_completo`
- `kb_notion`
- `llm-benchmark-results`
- `llm-benchmark-summary`
- `logs-cloud_security_posture.scores-default`
- `logs-cloud_security_posture.vulnerabilities_latest-default`
- `logs-tenant`
- `logs-ti_abuse
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| ✅ search_02 🔗 | 💬 medium | tool_usage | Search for orders from customers in Madrid in… | 53.1s | $0.1886 | 9.3 | 9.6 | 10.0 | 10.0 | 8.43 | 7 | |
❓ PreguntaSearch for orders from customers in Madrid in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should search benchmark-ecommerce filtering by customer.city=Madrid. There are exactly 49 orders, all from a single customer: María García López. The agent should return matching documents or a summary of them. 📋 Claim Decomposition📋 Claim Analysis
7 claims (4 central) · all grounded ●There are 49 orders from customers in Madrid in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Matches the exact expected answer and ground truth (49). The tool output shows results for the Madrid filter and is consistent with a total of 49 orders. ●All returned Madrid orders belong to a single customer: María García López. ✓ Fully Supported⚓ Grounded Ground truth explicitly states all 49 orders are from a single customer (María García López). Tool output rows shown all have `customer.name` = María García López and indicate a consistent customer across results (remaining rows are truncated but consistent with the summary). ●The customer email for these orders is maria.garcia@email.com. ✓ Not Verifiable⚓ Grounded Ground truth does not specify the email, so correctness vs ground truth can’t be confirmed. The tool output explicitly shows `customer.email` = maria.garcia@email.com in the visible rows. ○These orders span from July to December 2025. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not state the date range. Tool output shows December 2025 dates in the visible snippet, but the July start is not visible due to truncation (though it may exist in the truncated portion). ●Order ORD-2025-00037 on 2025-12-27 was for 'Cafetera Automática Deluxe' totaling €1,059.61 with status 'Cancelled'. ✓ Not Verifiable⚓ Grounded Ground truth does not provide per-order line items, so correctness can’t be checked against it. Tool output explicitly contains ORD-2025-00037 with the same date/time, product, total_amount (1059.6099...), and status 'cancelled' (agent’s € formatting/casing is a presentation change). ○The highest value order is ORD-2025-00550 — Smartphone X12 at €5,768.34. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not mention any 'highest value order.' The tool output is truncated, so this could be derived from the full returned rows but cannot be directly confirmed from the visible portion. ○Most frequently ordered products include Cafetera Automática Deluxe (5x), USB-C Hub 7-in-1 (5x), and Smartphone X12 (4x). ✓ Not Verifiable⚓ Partially Grounded Ground truth provides no product frequency counts. Tool output includes product names but is truncated, so the stated counts may be computed from the complete (non-visible) result set but aren’t directly verifiable from the snippet. 🧑⚖️ Judge ReasoningThe agent delivered the exact expected count (49) and correctly identified that all orders are from a single customer, aligning with ground truth and supported by the tool output. Most additional details (date span, highest-value order, frequency counts) are not verifiable against the provided ground truth and are only partially verifiable from the truncated tool output, but they do not conflict with the central requirement.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 49FOUND: 49📊 Todas las puntuacionesCorrectness 9.3 Groundedness 9.6 Relevance 5.7 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 3.5 Cost 1.0 Err Rate 10.0 Overall 8.4 💬 Respuesta del agente (extracto)Here are the orders from customers in Madrid found in the `benchmark-ecommerce` index:
**49 orders** were returned, all belonging to a single customer: **María García López** (maria.garcia@email.com), spanning from July to December 2025.
---
### Orders (sorted by most recent)
| Order ID | Date | Product | Total | Status |
|---|---|---|---|---|
| ORD-2025-00037 | 2025-12-27 | Cafetera Automática Deluxe | €1,059.61 | Cancelled |
| ORD-2025-00606 | 2025-12-26 | Mechanical Keyboard RGB | €606.96 | Returned |
| ORD-2025-00352 | 2025-12-21 | Cafetera Automática Deluxe | €1,522.69 | Delivered |
|
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| ✅ search_03 🔗 | 💬 easy | tool_usage | Show me the mapping of the 'benchmark-ecommer… | 15.7s | $0.0442 | 7.7 | 9.1 | 10.0 | 9.0 | 8.52 | 9 1✗ 1? | |
❓ PreguntaShow me the mapping of the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should use the get_index_mapping tool and display the field types and structure of the benchmark-ecommerce index. Key fields include: order_id (keyword), customer (object with name/text, city/keyword, country/keyword, email/keyword), product (object with name/text, category/keyword, subcategory/keyword), status (keyword), total_amount (float), order_date (date), payment_method (keyword), shipping_method (keyword), priority (keyword), quantity (integer), discount_pct (float), rating (float), tags (keyword), notes (text). 📋 Claim Decomposition📋 Claim Analysis
9 claims (7 central) · 1 contradicted · 1 ungrounded ●The index `benchmark-ecommerce` has a `customer` object with fields `city` (keyword), `country` (keyword), `email` (keyword), and `name` (text with a keyword multi-field). ✓ Fully Supported⚓ Grounded Ground truth lists the same customer structure and types; tool output shows `customer` properties with the specified mappings including `name` as `text` with `fields.keyword`. ●The index `benchmark-ecommerce` has a `product` object with fields `category` (keyword), `name` (text with a keyword multi-field), and `subcategory` (keyword). ✓ Fully Supported⚓ Grounded Matches ground truth product structure; tool output shows `product` properties with the stated field types and multi-field on `name`. ●Field `order_id` is mapped as `keyword` and `order_date` is mapped as `date`. ✓ Fully Supported⚓ Grounded Both fields and types are listed in ground truth and appear identically in the tool output mapping. ●Fields `delivered_at` and `estimated_delivery` are mapped as `date`. ✓ Not Verifiable⚓ Grounded Tool output explicitly shows both as `date`; the provided ground truth summary does not mention these fields, so correctness vs ground truth cannot be fully verified. ●Fields `status`, `priority`, `payment_method`, and `shipping_method` are mapped as `keyword`. ✓ Fully Supported⚓ Grounded All four are listed as keywords in the ground truth and confirmed as `keyword` in the tool output. ●Fields `quantity` (integer), `unit_price` (float), `total_amount` (float), `shipping_cost` (float), `tax_amount` (float), `discount_pct` (float), `is_gift` (boolean), `tags` (keyword), and `notes` (text) are present with those types. ✓ Not Verifiable⚓ Grounded Tool output includes all these fields with the stated types; ground truth includes most of them (quantity/discount_pct/total_amount/tags/notes) but not all (e.g., unit_price/shipping_cost/tax_amount/is_gift), making full verification vs ground truth incomplete. ●Field `rating` is mapped as `integer`. ✓ Contradicted⚓ Grounded [major] Tool output shows `rating` as `integer`, but the ground truth expects `rating` to be `float`, so this conflicts with the expected answer even though it matches the tool output. ○`customer.name` and `product.name` being `text` plus `keyword` supports full-text search and exact-match/aggregations. ✓ Not Verifiable⚓ Partially Grounded The multi-field mapping itself is grounded in the tool output; the behavioral interpretation (full-text vs aggregations) is standard Elasticsearch semantics but not explicitly stated in the tool output or ground truth. ○`tags` is an array-compatible keyword field suitable for filtering and aggregations. ✓ Not Verifiable⚓ Ungrounded [minor] Tool output only states `tags` is `keyword`; it does not mention array-compatibility or use-cases. The claim reflects general Elasticsearch behavior but isn’t grounded in the provided tool output. 🧑⚖️ Judge ReasoningThe agent accurately and clearly summarized the mapping returned by the get_index_mapping tool, listing most fields and types correctly. The main issue relative to the provided ground truth is `rating`, which the agent stated as integer (matching the tool output) while the ground truth expected float. A couple of interpretive observations (especially about `tags` array-compatibility) are not directly grounded in the tool output.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 7.7 Groundedness 9.1 Relevance 7.8 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 6.9 Cost 5.8 Err Rate 10.0 Overall 8.5 💬 Respuesta del agente (extracto)Here is the mapping for the **`benchmark-ecommerce`** index:
---
### Customer (object)
| Field | Type |
|---|---|
| `customer.city` | `keyword` |
| `customer.country` | `keyword` |
| `customer.email` | `keyword` |
| `customer.name` | `text` + `keyword` (multi-field) |
---
### Product (object)
| Field | Type |
|---|---|
| `product.category` | `keyword` |
| `product.name` | `text` + `keyword` (multi-field) |
| `product.subcategory` | `keyword` |
---
### Order Fields
| Field | Type |
|---|---|
| `order_id` | `keyword` |
| `order_date` | `date` |
| `delivered_at` | `date` |
| `estimated_deli
| ||||||||||||
| ✅ search_04 🔗 | 💬 hard | tool_usage | Find all cancelled orders in the 'benchmark-e… | 39.6s | $0.1684 | 9.5 | 8.4 | 10.0 | 9.0 | 8.18 | 7 1? | |
❓ PreguntaFind all cancelled orders in the 'benchmark-ecommerce' index that have a total_amount greater than 500
🎯 GROUND TRUTH:
The agent should search for documents where status=cancelled and total_amount>500 in the benchmark-ecommerce index. There are exactly 40 such orders. 📋 Claim Decomposition📋 Claim Analysis
7 claims (3 central) · 1 ungrounded ●There are 40 cancelled orders in the 'benchmark-ecommerce' index with total_amount greater than 500. ✓ Fully Supported⚓ Grounded Matches the provided ground truth exactly (expected answer: 40). The agent’s count is also consistent with the tool query/output context, even though the returned rows are truncated. ●The matching criteria used are status = "cancelled" and total_amount > 500 in the benchmark-ecommerce index. ✓ Fully Supported⚓ Grounded This is exactly the ground-truth filter and is explicitly shown in the tool ES|QL (WHERE status == "cancelled" AND total_amount > 500). ○The results are sorted by total_amount descending. ✓ Not Verifiable⚓ Grounded Ground truth does not require or confirm sorting, but the tool ES|QL includes 'SORT total_amount DESC', supporting the agent’s statement. ●The agent provided the full list of all 40 matching orders with their order_id, order_date, customer name, and total amount. ✓ Not Verifiable⚓ Partially Grounded Ground truth only confirms the count (40), not the specific 40 IDs/details. Tool output is truncated, but it does show that these fields were selected and at least the top portion of the table aligns with the agent’s rows. ○The highest value matching order is ORD-2025-00761 with a total_amount of $7,350.64 (customer Javier Ruiz Hernández). ✓ Not Verifiable⚓ Grounded Not asserted by ground truth, but the first row visible in tool output matches order_id ORD-2025-00761, customer Javier Ruiz Hernández, and total_amount 7350.640136... (consistent with $7,350.64). ○The lowest value matching order is ORD-2025-00968 with a total_amount of $523.99 (customer Giuseppe Rossi). ✓ Not Verifiable⚓ Partially Grounded Ground truth does not specify the minimum. Tool output is truncated and does not show the bottom of the result set, so the specific lowest order cannot be directly confirmed from the visible tool data. ○Most affected customers by cancelled order count include Sofia Torres Navarro (6), Javier Ruiz Hernández (4), Giuseppe Rossi (4), Ana Fernández Ruiz (3), and John Smith (3). ✓ Not Verifiable⚓ Ungrounded [major] Ground truth provides no customer frequency breakdown, and the tool output does not provide aggregation/counts by customer (only row-level results, and truncated), so these customer counts are not supported by the tool output. 🧑⚖️ Judge ReasoningThe core answer (40) exactly matches the ground truth and the filtering logic is explicitly supported by the tool query. Most row-level details appear plausible given the truncated tool output and selected fields, but customer frequency statistics are not supported by the tool output and are therefore ungrounded.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 40FOUND: 40📊 Todas las puntuacionesCorrectness 9.5 Groundedness 8.4 Relevance 4.3 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 4.5 Cost 1.0 Err Rate 10.0 Overall 8.2 💬 Respuesta del agente (extracto)Here are all **40 cancelled orders** with a `total_amount` greater than $500, sorted by amount descending:
| Order ID | Order Date | Customer | Total Amount |
|---|---|---|---|
| ORD-2025-00761 | Nov 10, 2025 | Javier Ruiz Hernández | $7,350.64 |
| ORD-2025-00423 | Jul 27, 2025 | John Smith | $6,087.85 |
| ORD-2025-00238 | Jul 18, 2025 | Hans Mueller | $5,216.98 |
| ORD-2025-00953 | Jul 20, 2025 | Pierre Dupont | $3,618.73 |
| ORD-2025-00513 | Nov 18, 2025 | Javier Ruiz Hernández | $3,602.81 |
| ORD-2025-00014 | Oct 25, 2025 | John Smith | $2,841.48 |
| ORD-2025-00622 | Sep 3, 2025 | Javier R
| ||||||||||||
| Test | Type | Category | Question | Latency | Cost | Corr | Grnd | Tool | Instr | Overall | Claims | Tools |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ✅ adversarial_01 🔗 | 💬 hard | adversarial | Show me the distribution of the 'discount_typ… | 111.4s | $0.0516 | 8.8 | 10.0 | 10.0 | 10.0 | 8.58 | 8 | |
❓ PreguntaShow me the distribution of the 'discount_type' field in the 'benchmark-ecommerce' index. Group by discount_type and show counts.
🎯 GROUND TRUTH:
The field 'discount_type' does NOT exist in the index (the actual field is 'discount_pct'). The agent should either report that the field doesn't exist, suggest the correct field, or handle the error gracefully. Tests handling of non-existent fields. 📋 Claim Decomposition📋 Claim Analysis
8 claims (8 central) · all grounded ●The `benchmark-ecommerce` index does not contain a field named `discount_type`. ✓ Fully Supported⚓ Grounded Ground truth states `discount_type` does NOT exist, and the index mapping tool output lists `discount_pct` but not `discount_type`. ●The available discount-related field in the index schema is `discount_pct`. ✓ Fully Supported⚓ Grounded Ground truth identifies `discount_pct` as the actual field; tool mapping output includes `discount_pct`. ●The distribution for `discount_pct` includes a row where `discount_pct` = 0 with count 376. ✓ Not Verifiable⚓ Grounded Exact counts are not specified in ground truth, but tool tabular output shows (376, 0) for discount_pct. ●The distribution for `discount_pct` includes a row where `discount_pct` = 5 with count 144. ✓ Not Verifiable⚓ Grounded Ground truth does not provide counts; tool output shows (144, 5). ●The distribution for `discount_pct` includes a row where `discount_pct` = 15 with count 132. ✓ Not Verifiable⚓ Grounded Counts are not in ground truth; tool output shows (132, 15). ●The distribution for `discount_pct` includes a row where `discount_pct` = 10 with count 118. ✓ Not Verifiable⚓ Grounded Counts are not in ground truth; tool output shows (118, 10). ●The distribution for `discount_pct` includes a row where `discount_pct` = 25 with count 116. ✓ Not Verifiable⚓ Grounded Counts are not in ground truth; tool output shows (116, 25). ●The distribution for `discount_pct` includes a row where `discount_pct` = 20 with count 114. ✓ Not Verifiable⚓ Grounded Counts are not in ground truth; tool output shows (114, 20). 🧑⚖️ Judge ReasoningThe agent correctly identified that `discount_type` does not exist and appropriately pivoted to the actual field `discount_pct`, matching the ground truth and the index mapping. The reported distribution values and counts are fully grounded in the tool’s ES|QL tabular outputs. Formatting and instruction handling (graceful error + suggested correct field + counts) are strong.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 8.8 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 1.0 Cost 5.4 Err Rate 10.0 Overall 8.6 💬 Respuesta del agente (extracto)The `benchmark-ecommerce` index does not contain a field named `discount_type`. The available discount-related field in the index schema is `discount_pct`.
Here is the distribution for the `discount_pct` field:
| discount_pct | count |
| :--- | :--- |
| 0 | 376 |
| 5 | 144 |
| 15 | 132 |
| 10 | 118 |
| 25 | 116 |
| 20 | 114 |
| ||||||||||||
| ❌ adversarial_02 🔗 | 💬 hard | adversarial | Find all orders in 'benchmark-ecommerce' wher… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaFind all orders in 'benchmark-ecommerce' where the status is both 'cancelled' AND 'delivered' at the same time
🎯 GROUND TRUTH:
This is a contradictory request — an order cannot be both cancelled and delivered. The result should be 0 orders. The agent should recognize and communicate this contradiction or return empty results gracefully. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ❌ adversarial_03 🔗 | 💬 expert | adversarial | Perform a SQL JOIN between the 'benchmark-eco… | 120.2s | — | ⏱ Timeout | ||||||
❓ PreguntaPerform a SQL JOIN between the 'benchmark-ecommerce' and 'benchmark-customers' indices on the customer email field and return the top 5 results
🎯 GROUND TRUTH:
Elasticsearch does not support SQL JOINs natively. The agent should explain this limitation and potentially suggest an alternative approach (e.g., querying both indices separately, or using ES|QL LOOKUP). Tests handling of impossible operations. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ❌ cross_01 🔗 | 💬 expert | cross_index | First, find the 'Gold' tier customers in 'ben… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaFirst, find the 'Gold' tier customers in 'benchmark-customers'. Then, search for their orders in 'benchmark-ecommerce' by matching customer name. How many orders do Gold tier customers have in total?
🎯 GROUND TRUTH:
The agent must: 1) Query benchmark-customers WHERE tier='Gold' — there are 2 Gold customers: Laura Martínez Gómez and Sofia Torres Navarro, 2) Search benchmark-ecommerce for their orders: Laura has 54 orders and Sofia has 58 orders, 3) Total Gold tier orders: 112. Tests cross-index reasoning requiring multiple tool calls. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ cross_02 🔗 | 💬 hard | cross_index | Show me the benchmark-customers index mapping… | 53.2s | $0.0218 | 10.0 | 10.0 | 10.0 | 10.0 | 9.19 | 6 | |
❓ PreguntaShow me the benchmark-customers index mapping. Then tell me: which customer tier (Standard, Premium, Gold, Platinum) has the most customers?
🎯 GROUND TRUTH:
The agent should get the mapping of benchmark-customers, then query to aggregate by tier. The exact distribution is: Standard (11 customers, 55%), Premium (6 customers, 30%), Gold (2 customers, 10%), Platinum (1 customer, 5%). Standard has the most customers. There are 20 customers total. Tests discovery and querying of a new index. 📋 Claim Decomposition📋 Claim Analysis
6 claims (6 central) · all grounded ●The benchmark-customers index mapping defines fields: age (integer), city (keyword), country (keyword), customer_id (keyword), email (keyword), is_active (boolean), lifetime_value (float), name (text with keyword subfield), notes (text), phone (keyword), preferred_category (keyword), preferred_payment (keyword), registration_date (date), and tier (keyword). ✓ Fully Supported⚓ Grounded This matches both the provided tool output mapping properties and the expected behavior in the ground truth (retrieve mapping). ●Standard tier has 11 customers. ✓ Fully Supported⚓ Grounded The tool output shows [11, "Standard"], and the ground truth specifies Standard has 11 customers. ●Premium tier has 6 customers. ✓ Fully Supported⚓ Grounded The tool output shows [6, "Premium"], matching the ground truth count of 6. ●Gold tier has 2 customers. ✓ Fully Supported⚓ Grounded The tool output shows [2, "Gold"], matching the ground truth count of 2. ●Platinum tier has 1 customer. ✓ Fully Supported⚓ Grounded The tool output shows [1, "Platinum"], matching the ground truth count of 1. ●Standard has the most customers, with 11 customers. ✓ Fully Supported⚓ Grounded Both the tool output counts and the ground truth distribution indicate Standard is the largest tier (11). 🧑⚖️ Judge ReasoningThe agent correctly presented the benchmark-customers index mapping consistent with the tool output and accurately reported the customer counts by tier. The conclusion that Standard has the most customers is supported by both the aggregation results and the ground truth distribution.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 3.5 Cost 6.9 Err Rate 10.0 Overall 9.2 💬 Respuesta del agente (extracto)## benchmark-customers Index Mapping
```json
{
"mappings": {
"properties": {
"age": {"type": "integer"},
"city": {"type": "keyword"},
"country": {"type": "keyword"},
"customer_id": {"type": "keyword"},
"email": {"type": "keyword"},
"is_active": {"type": "boolean"},
"lifetime_value": {"type": "float"},
"name": {"type": "text", "fields": {"keyword": {"type": "keyword"}}},
"notes": {"type": "text"},
"phone": {"type": "keyword"},
"preferred_category": {"type": "keyword"},
"preferred_payment": {"type": "keyword"},
| ||||||||||||
| ✅ edge_01 🔗 | 💬 easy | edge_case | Search for documents about 'quantum_headphone… | 108.7s | $0.0419 | 9.6 | 8.9 | 10.0 | 10.0 | 8.19 | 9 1? | |
❓ PreguntaSearch for documents about 'quantum_headphones_xyz_404' in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should attempt the search and gracefully report that no results were found for this nonexistent product. Tests zero-result handling. 📋 Claim Decomposition📋 Claim Analysis
9 claims (2 central) · 1 ungrounded ●No documents matching 'quantum_headphones_xyz_404' were found in the 'benchmark-ecommerce' index. ✓ Fully Supported⚓ Grounded Matches the ground truth expectation (zero results handling) and is supported by the ES|QL search on product.name returning empty columns/values. ○The 'benchmark-ecommerce' index exists and is accessible. ✓ Not Verifiable⚓ Grounded Ground truth does not mention index existence explicitly, but tool output from list_indices shows an index named 'benchmark-ecommerce', supporting existence/access. ○Multiple search approaches were attempted (exact match, product name field search, and broader term searches). ✓ Not Verifiable⚓ Grounded Ground truth only requires attempting a search; tool logs show searches for the raw query, a field-scoped query (product.name), and broader queries ('quantum', 'headphones'). ●None of the attempted searches returned results for 'quantum_headphones_xyz_404'. ✓ Fully Supported⚓ Grounded Consistent with expected behavior and supported by the product.name ES|QL query returning no rows and the initial search output being empty. ○No results were returned even for the term 'quantum' in the 'benchmark-ecommerce' index. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not address the 'quantum' term. Tool output for the 'quantum' search is empty, which suggests zero results, but the raw output lacks an explicit '0 hits' indicator. ○The index contains headphone products, for example 'Wireless Headphones ANC'. ✓ Not Verifiable⚓ Grounded Not required by ground truth, but tool output shows multiple 'headphones' search hits highlighting 'Wireless Headphones ANC' and a retrieved document whose product.name is 'Wireless Headphones ANC'. ○Multiple orders were found for 'Wireless Headphones ANC'. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not cover this. Tool output shows multiple search hit resources with the same highlight text, implying multiple matching documents, but only one full document was fetched, so 'multiple orders' is inferred rather than fully demonstrated. ○The dataset uses natural product names rather than SKU-style identifiers. ✓ Not Verifiable⚓ Ungrounded [minor] This is an interpretive generalization not directly supported by the provided tool outputs (which show at least one natural product name, but do not establish the absence of SKU-style identifiers across the dataset). ○The product 'quantum_headphones_xyz_404' may not exist in this dataset, or it may be stored under a different name/format, or in a different index. ✓ Not Verifiable⚓ Disclosed Ungrounded These are presented as 'possible reasons' (speculation/advice) rather than asserted facts; they are not directly evidenced by the tool output. 🧑⚖️ Judge ReasoningThe agent delivered the expected zero-results outcome for the nonexistent product, which aligns with the ground truth and is supported by the tool outputs. Most additional statements about attempted searches and the presence of headphone products are grounded in the logs, while broader generalizations (e.g., naming conventions across the dataset) are not directly supported and are mildly ungrounded.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search platform.core.list_indices platform.core.get_index_mapping platform.core.get_document_by_id
📊 Todas las puntuacionesCorrectness 9.6 Groundedness 8.9 Relevance 2.2 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 1.0 Cost 5.9 Err Rate 10.0 Overall 8.2 💬 Respuesta del agente (extracto)No documents matching 'quantum_headphones_xyz_404' were found in the 'benchmark-ecommerce' index.
**Search Summary:**
- The index exists and is accessible
- Multiple search approaches were attempted (exact match, product name field search, and broader term searches)
- None returned results for 'quantum_headphones_xyz_404' or even the term 'quantum'
**What Does Exist:**
The index does contain headphone products, but they use natural product names rather than SKU-style identifiers. For example:
- "Wireless Headphones ANC" (multiple orders found)
**Possible Reasons:**
1. The product 'quantum_h
| ||||||||||||
| ✅ edge_02 🔗 | 💬 medium | edge_case | show me everything about the ecommerce data | 94.0s | $0.0725 | 9.5 | 9.5 | 10.0 | 8.0 | 8.33 | 8 | |
❓ Preguntashow me everything about the ecommerce data
🎯 GROUND TRUTH:
The agent should handle this vague request by making a reasonable interpretation — perhaps showing a sample of documents, the mapping, or a summary of the benchmark-ecommerce index. 📋 Claim Decomposition📋 Claim Analysis
8 claims (7 central) · all grounded ●The cluster contains an index called `benchmark-ecommerce`. ✓ Fully Supported⚓ Grounded Tool output from `list_indices` includes `benchmark-ecommerce`, and `get_index_mapping` was retrieved for that index. ●The `benchmark-ecommerce` index contains order-related data (e.g., customer, product, order, pricing, shipping fields). ✓ Partially Supported⚓ Partially Grounded The mapping clearly shows order/customer/product/pricing/shipping-style fields, supporting the general characterization; however, the tool output does not explicitly label the dataset as 'order data' beyond field names. ●The mapping includes customer fields with types: `customer.city` keyword, `customer.country` keyword, `customer.email` keyword, and `customer.name` text with a `keyword` subfield. ✓ Fully Supported⚓ Grounded These exact fields and types appear in the `get_index_mapping` output under `customer.properties`. ●The mapping includes order fields with types: `order_id` keyword, `order_date` date, `status` keyword, `priority` keyword, `notes` text, `tags` keyword, and `is_gift` boolean. ✓ Fully Supported⚓ Grounded All listed fields and types match the `get_index_mapping` output. ●The mapping includes product fields with types: `product.name` text with a `keyword` subfield, `product.category` keyword, and `product.subcategory` keyword. ✓ Fully Supported⚓ Grounded These fields and types are present in the `product.properties` section of the mapping. ●The mapping includes pricing/financial fields with types: `unit_price` float, `quantity` integer, `total_amount` float, `tax_amount` float, `shipping_cost` float, and `discount_pct` float. ✓ Fully Supported⚓ Grounded All listed fields and types match the mapping output. ●The mapping includes shipping/delivery fields with types: `shipping_method` keyword, `payment_method` keyword, `estimated_delivery` date, and `delivered_at` date. ✓ Fully Supported⚓ Grounded These fields and types are explicitly shown in the mapping output. ○This index supports queries across all these dimensions (e.g., filtering by customer location, product category, order status, date ranges, payment methods). ✓ Not Verifiable⚓ Disclosed Ungrounded While generally true that indexed fields can be queried/filtered, the tool output does not demonstrate query support or provide examples/results; it’s presented as a general capability rather than a tool-confirmed result. 🧑⚖️ Judge ReasoningThe response appropriately interprets a vague request by summarizing the `benchmark-ecommerce` index mapping, which is consistent with the expected handling. Most field/type statements are directly grounded in the mapping tool output and are correct. The only notable ungrounded element is the generalized claim about query support, which is reasonable but not evidenced by returned search results.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search platform.core.list_indices platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 9.5 Groundedness 9.5 Relevance 8.8 Format 9.0 Tool Calling 10.0 Instruct. 8.0 Latency 1.0 Cost 4.4 Err Rate 10.0 Overall 8.3 💬 Respuesta del agente (extracto)# Ecommerce Data Overview
The cluster contains an index called **`benchmark-ecommerce`** with comprehensive order data. Here's the complete structure:
## Data Fields
### Customer Information
| Field | Type |
|-------|------|
| `customer.name` | Text + Keyword |
| `customer.email` | Keyword |
| `customer.city` | Keyword |
| `customer.country` | Keyword |
### Order Details
| Field | Type |
|-------|------|
| `order_id` | Keyword |
| `order_date` | Date |
| `status` | Keyword |
| `priority` | Keyword |
| `notes` | Text |
| `tags` | Keyword |
| `is_gift` | Boolean |
### Product Information
|
| ||||||||||||
| ✅ edge_03 🔗 | 💬 easy | edge_case | Get the mapping for an index called 'this_ind… | 20.8s | $0.0082 | 10.0 | 9.2 | 10.0 | 10.0 | 9.16 | 4 | |
❓ PreguntaGet the mapping for an index called 'this_index_definitely_does_not_exist_12345'
🎯 GROUND TRUTH:
The agent should attempt to get the mapping and gracefully handle the error when the index doesn't exist. 📋 Claim Decomposition📋 Claim Analysis
4 claims (4 central) · all grounded ●The index `this_index_definitely_does_not_exist_12345` does not exist. ✓ Fully Supported⚓ Grounded Tool output returns an index_not_found_exception for this exact index name, supporting that it does not exist; this matches the expected behavior in the ground truth. ●Attempting to retrieve the mapping returned an `index_not_found_exception`. ✓ Fully Supported⚓ Grounded The tool output explicitly shows an error with message containing `index_not_found_exception` when calling get_index_mapping. ●The exception indicates "no such index". ✓ Fully Supported⚓ Grounded The tool output includes the phrase "no such index [this_index_definitely_does_not_exist_12345]", which directly supports the claim. ●Since the index is not present in the cluster, there is no mapping available to display. ✓ Fully Supported⚓ Partially Grounded The tool output confirms the index is missing; concluding that a mapping cannot be displayed is a reasonable inference from the error, though the tool output does not explicitly state 'no mapping available' as a separate fact. 🧑⚖️ Judge ReasoningThe agent correctly attempted to fetch the index mapping and accurately summarized the resulting index_not_found_exception. All central facts are supported by the tool output, with only a minor inferential step about the absence of a mapping. The response follows instructions and is clearly formatted for the user.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 9.2 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 10.0 Latency 6.4 Cost 9.4 Err Rate 7.0 Overall 9.2 💬 Respuesta del agente (extracto)The index `this_index_definitely_does_not_exist_12345` does not exist.
Attempting to retrieve the mapping returned an `index_not_found_exception` indicating "no such index". Since the index is not present in the cluster, there is no mapping available to display.
| ||||||||||||
| ✅ esql_01 🔗 | 💬 easy | analytics | Using ES|QL, count how many orders are in the… | 36.3s | $0.0191 | 10.0 | 10.0 | 10.0 | 10.0 | 9.10 | 2 | |
❓ PreguntaUsing ES|QL, count how many orders are in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should query the benchmark-ecommerce index and return 1000 as the document count. The tool may use ES|QL internally via platform.core.search. 📋 Claim Decomposition📋 Claim Analysis
2 claims (1 central) · all grounded ●There are 1000 orders in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Matches the ground truth expected answer (1000) and is directly supported by the tool output showing COUNT(*) = 1000 for the index. ○The ES|QL query used to retrieve this count is: `FROM benchmark-ecommerce | STATS COUNT(*)`. ✓ Fully Supported⚓ Grounded The tool output explicitly shows the ES|QL query `FROM benchmark-ecommerce | STATS COUNT(*)`, and it is an appropriate query to obtain the document count. 🧑⚖️ Judge ReasoningThe agent provided the exact expected count (1000) and included the exact ES|QL query reflected in the tool output. All factual claims are fully correct and grounded, and the response follows the user’s instruction and formatting requirements.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 1000FOUND: 1000📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 5.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 4.9 Cost 7.2 Err Rate 10.0 Overall 9.1 💬 Respuesta del agente (extracto)There are **1000** orders in the `benchmark-ecommerce` index.
The ES|QL query used to retrieve this count is:
```esql
FROM benchmark-ecommerce
| STATS COUNT(*)
```
| ||||||||||||
| ✅ esql_02 🔗 | 💬 hard | analytics | Using ES|QL on the 'benchmark-ecommerce' inde… | 65.9s | $0.0259 | 8.1 | 9.9 | 10.0 | 9.0 | 8.33 | 9 | |
❓ PreguntaUsing ES|QL on the 'benchmark-ecommerce' index, show me the number of orders and total revenue per product category, sorted by revenue descending
🎯 GROUND TRUTH:
The agent should group by product.category, calculate COUNT(*) and SUM(total_amount), then sort by total revenue descending. The exact results are: electronics (413 orders, €686,037.84), home (203 orders, €137,010.55), clothing (186 orders, €45,103.57), sports (42 orders, €20,303.18), bags (52 orders, €16,039.01), books (104 orders, €15,702.88). Electronics is the top category by a wide margin. 📋 Claim Decomposition📋 Claim Analysis
9 claims (6 central) · all grounded ●An ES|QL query to answer the question is: FROM benchmark-ecommerce | STATS order_count = COUNT(*), total_revenue = SUM(total_amount) BY product.category | SORT total_revenue DESC. ✓ Fully Supported⚓ Grounded This matches the expected approach in the Ground Truth and is exactly the query shown in the tool output (minus the tool-added LIMIT 100). ●Electronics has 413 orders and total revenue of 686,037.84. ✓ Partially Supported⚓ Grounded Order count and revenue match Ground Truth/tool output (686037.8392...), but the agent formats it with a dollar sign whereas Ground Truth expresses amounts in euros. ●Home has 203 orders and total revenue of 137,010.55. ✓ Partially Supported⚓ Grounded Numbers match Ground Truth/tool output (137010.5497...), but currency symbol differs ($ vs €). ●Clothing has 186 orders and total revenue of 45,103.57. ✓ Partially Supported⚓ Grounded Numbers match Ground Truth/tool output (45103.5699...), but currency symbol differs ($ vs €). ●Sports has 42 orders and total revenue of 20,303.18. ✓ Partially Supported⚓ Grounded Numbers match Ground Truth/tool output (20303.1800...), but currency symbol differs ($ vs €). ●Bags has 52 orders and total revenue of 16,039.01, and Books has 104 orders and total revenue of 15,702.88. ✓ Partially Supported⚓ Grounded All counts and totals match Ground Truth/tool output (16039.0099..., 15702.8799...), but currency symbol differs ($ vs €). ○Electronics dominates with 413 orders and about $686K in revenue (over 5x the next category). ✓ Partially Supported⚓ Partially Grounded Dominance and counts/totals are supported by the tool output, but 'over 5x' is slightly imprecise (686,037.84 is ~5.01x 137,010.55), and the dollar currency is not supported by Ground Truth. ○Books has relatively high order volume (104) but lower average order value. ✓ Fully Supported⚓ Grounded Order volume (104) is in tool output, and the lower average order value follows from the provided totals (Books revenue per order is low compared with other categories). ○Sports has the fewest orders and higher revenue per order than Bags and Books. ✓ Fully Supported⚓ Grounded Tool output shows Sports has the smallest order_count (42). Derived revenue-per-order comparisons (Sports > Bags and Books) are consistent with the tool totals and counts. 🧑⚖️ Judge ReasoningThe agent produced the correct ES|QL structure (STATS by product.category with COUNT and SUM, sorted by total revenue) and reported results that match the tool output and Ground Truth numerically. The main issue is a currency mismatch (agent uses $ while Ground Truth uses €) and a minor imprecision in the 'over 5x' phrasing, but the core answer is correct and well formatted.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.1 Groundedness 9.9 Relevance 6.7 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 2.6 Cost 6.7 Err Rate 10.0 Overall 8.3 💬 Respuesta del agente (extracto)Here are the order counts and total revenue per product category from the `benchmark-ecommerce` index:
## ES|QL Query
```esql
FROM benchmark-ecommerce
| STATS order_count = COUNT(*), total_revenue = SUM(total_amount) BY product.category
| SORT total_revenue DESC
```
## Results
| Product Category | Order Count | Total Revenue |
|-----------------|-------------|---------------|
| Electronics | 413 | $686,037.84 |
| Home | 203 | $137,010.55 |
| Clothing | 186 | $45,103.57 |
| Sports | 42 | $20,303.18 |
| Bags | 52 | $16,039.01 |
| Books | 104 | $15,702.88 |
**Key Insights:**
- **Electronics*
| ||||||||||||
| ✅ esql_03 🔗 | 💬 hard | analytics | Using ES|QL, show me the average order value … | 40.0s | $0.0234 | 8.0 | 8.1 | 10.0 | 9.0 | 8.25 | 8 | |
❓ PreguntaUsing ES|QL, show me the average order value by payment method in the 'benchmark-ecommerce' index, but only for delivered orders
🎯 GROUND TRUTH:
The agent should filter WHERE status=='delivered' (522 orders), then group by payment_method and calculate AVG(total_amount). Expected averages: credit_card €895.16 (167 orders), paypal €889.08 (136 orders), debit_card €852.93 (110 orders), bank_transfer €764.34 (63 orders), cash_on_delivery €744.24 (46 orders). 📋 Claim Decomposition📋 Claim Analysis
8 claims (7 central) · all grounded ●The correct ES|QL query is: FROM benchmark-ecommerce | WHERE status == "delivered" | STATS avg_order_value = AVG(total_amount) BY payment_method | LIMIT 100. ✓ Fully Supported⚓ Grounded This matches the ground-truth approach (filter delivered, group by payment_method, compute AVG(total_amount)) and exactly matches the ES|QL shown in the tool output. ●The query filters to only delivered orders using WHERE status == "delivered". ✓ Fully Supported⚓ Grounded Ground truth explicitly requires filtering status=='delivered', and the tool-output ES|QL includes that clause. ●Average order value for credit_card delivered orders is $895.16. ✓ Partially Supported⚓ Partially Grounded [minor] The numeric average (895.1640...) matches both ground truth (€895.16) and tool output, but the dollar currency symbol is not supported by ground truth (which uses €) nor by tool output (which provides no currency). ●Average order value for paypal delivered orders is $889.08. ✓ Partially Supported⚓ Partially Grounded [minor] The numeric average (889.0773...) matches ground truth (€889.08) and tool output, but the $ symbol is not supported (ground truth is €; tool output has no currency). ●Average order value for debit_card delivered orders is $852.93. ✓ Partially Supported⚓ Partially Grounded [minor] The numeric average (852.9283...) matches ground truth (€852.93) and tool output, but the $ symbol is not supported by either ground truth or tool output. ●Average order value for bank_transfer delivered orders is $764.34. ✓ Partially Supported⚓ Partially Grounded [minor] The numeric average (764.3379...) matches ground truth (€764.34) and tool output, but the $ symbol is not supported by either ground truth or tool output. ●Average order value for cash_on_delivery delivered orders is $744.24. ✓ Partially Supported⚓ Partially Grounded [minor] The numeric average (744.2371...) matches ground truth (€744.24) and tool output, but the $ symbol is not supported by either ground truth or tool output. ○Among delivered orders, credit_card has the highest average order value and cash_on_delivery has the lowest. ✓ Fully Supported⚓ Grounded This is consistent with both the tool-output averages and the ground-truth averages (credit_card highest, cash_on_delivery lowest). 🧑⚖️ Judge ReasoningThe agent produced the correct ES|QL query and reported averages that match the tool output and the expected ground truth values. The main issue is the use of a dollar currency symbol, which is not supported by the tool output and conflicts with the ground truth’s euro-denominated values. Otherwise, the response is well-formatted and follows the user’s instruction to compute averages by payment method for delivered orders.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.0 Groundedness 8.1 Relevance 8.8 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 4.5 Cost 6.8 Err Rate 10.0 Overall 8.2 💬 Respuesta del agente (extracto)Here's the ES|QL query to calculate average order value by payment method for delivered orders:
```esql
FROM benchmark-ecommerce
| WHERE status == "delivered"
| STATS avg_order_value = AVG(total_amount) BY payment_method
| LIMIT 100
```
**Results:**
| Payment Method | Average Order Value |
|----------------|---------------------|
| credit_card | $895.16 |
| paypal | $889.08 |
| debit_card | $852.93 |
| bank_transfer | $764.34 |
| cash_on_delivery | $744.24 |
Credit card payments have the highest average order value among delivered orders at $895.16, while cash on delivery has the lowest at
| ||||||||||||
| ✅ exact_01 🔗 | 💬 medium | exact_answer | How many orders in the 'benchmark-ecommerce' … | 67.2s | $0.0226 | 10.0 | 10.0 | 10.0 | 10.0 | 9.10 | 1 | |
❓ PreguntaHow many orders in the 'benchmark-ecommerce' index have status 'shipped' AND total_amount greater than 1000? Give me only the count.
🎯 GROUND TRUTH:
The exact answer is 44. The agent should filter by status=shipped AND total_amount>1000 and return the count. This tests precise numeric accuracy. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●The count of orders in the 'benchmark-ecommerce' index with status 'shipped' and total_amount > 1000 is 44. ✓ Fully Supported⚓ Grounded The ground truth specifies the exact expected answer is 44, and the tool output (ES|QL STATS COUNT(*)) returns 44. 🧑⚖️ Judge ReasoningThe agent responded with only the count, as requested. The value exactly matches both the ground truth and the tool output, so the response is correct and fully grounded.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 44FOUND: 44📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 2.5 Cost 6.9 Err Rate 10.0 Overall 9.1 💬 Respuesta del agente (extracto)44
| ||||||||||||
| ✅ exact_02 🔗 | 💬 medium | exact_answer | What is the total sum of 'total_amount' for a… | 104.5s | $0.0311 | 7.9 | 10.0 | 10.0 | 7.0 | 8.11 | 3 1✗ | |
❓ PreguntaWhat is the total sum of 'total_amount' for all orders with status 'delivered' in the 'benchmark-ecommerce' index?
🎯 GROUND TRUTH:
The exact answer is approximately €446,617.23. The agent should aggregate SUM(total_amount) WHERE status='delivered'. Tests precision in numeric aggregation. 📋 Claim Decomposition📋 Claim Analysis
3 claims (3 central) · 1 contradicted · all grounded ●The response reports the total sum of `total_amount` for all orders with status `delivered` in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded This matches the intended aggregation described in the ground truth (SUM(total_amount) WHERE status='delivered') and is consistent with the ES|QL shown in the tool output. ●The total sum is 446,617.22955703735. ✓ Contradicted⚓ Grounded [minor] Tool output shows SUM(total_amount)=446617.22955703735, so the claim is grounded. However, the expected exact answer for this evaluation is 446617.23, and per the instructions this must be compared precisely; the values are not exactly the same (rounding/precision mismatch). ●The sum pertains specifically to orders with status `delivered` (as opposed to other statuses). ✓ Fully Supported⚓ Grounded Both the ground truth description and the tool query explicitly filter WHERE status == "delivered". 🧑⚖️ Judge ReasoningThe agent correctly identified the index, filter condition, and used the tool output value, so the response is well grounded. However, because this is an exact-answer test and the agent provided an unrounded value that does not exactly match 446617.23, the numeric claim is marked contradicted (minor severity due to rounding precision).
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 446617.23FOUND: 446617.22955703735📊 Todas las puntuacionesCorrectness 7.9 Groundedness 10.0 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 7.0 Latency 1.0 Cost 6.4 Err Rate 10.0 Overall 8.1 💬 Respuesta del agente (extracto)The total sum of `total_amount` for all orders with status `delivered` in the `benchmark-ecommerce` index is **446,617.22955703735**.
| ||||||||||||
| ✅ exact_03 🔗 | 💬 easy | exact_answer | How many unique customers (distinct customer … | 55.3s | $0.0175 | 10.0 | 10.0 | 10.0 | 10.0 | 9.21 | 2 | |
❓ PreguntaHow many unique customers (distinct customer names) exist in the 'benchmark-ecommerce' index?
🎯 GROUND TRUTH:
The exact answer is 20. The agent should count distinct customer.name.keyword values. Tests cardinality aggregation. 📋 Claim Decomposition📋 Claim Analysis
2 claims (2 central) · all grounded ●There are 20 unique customers in the 'benchmark-ecommerce' index. ✓ Fully Supported⚓ Grounded Ground truth specifies the exact answer is 20, and the tool output shows an ES|QL COUNT_DISTINCT(customer.name) result of 20. ●The 20 refers to distinct customer names. ✓ Fully Supported⚓ Grounded The user asked for distinct customer names and the tool output explicitly computed COUNT_DISTINCT(customer.name), matching the 'distinct customer names' interpretation. 🧑⚖️ Judge ReasoningThe agent’s response contains the correct distinct-customer count (20) and matches the expected exact answer. The claim is directly supported by the tool’s ES|QL output showing COUNT_DISTINCT(customer.name) = 20, and the response follows instructions with clear, minimal formatting.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 20FOUND: 20📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 3.3 Cost 7.5 Err Rate 10.0 Overall 9.2 💬 Respuesta del agente (extracto)There are **20 unique customers** (distinct customer names) in the 'benchmark-ecommerce' index.
| ||||||||||||
| ✅ expert_01 🔗 | 💬 expert | expert | In the 'benchmark-ecommerce' index, find the … | 56.8s | $0.0231 | 9.0 | 9.0 | 10.0 | 9.0 | 8.48 | 7 1✗ 1? | |
❓ PreguntaIn the 'benchmark-ecommerce' index, find the top 3 customers by total revenue from non-cancelled orders. For each customer, show their name, total revenue, number of orders, and average order value. Format as a table.
🎯 GROUND TRUTH:
The agent should filter out cancelled orders (889 non-cancelled orders), group by customer name, calculate SUM(total_amount), COUNT(*), and AVG(total_amount), sort by revenue descending, limit to 3. The exact top 3 are: 1) Hans Mueller (55 orders, €68,600.24 total, avg €1,247.28), 2) Elena Moreno Jiménez (47 orders, €55,882.65 total, avg €1,188.99), 3) Lucía Romero Gutiérrez (43 orders, €52,189.00 total, avg €1,213.70). Tests complex aggregation with derived metrics. 📋 Claim Decomposition📋 Claim Analysis
7 claims (5 central) · 1 contradicted · 1 ungrounded ●The top 3 customers by total revenue from non-cancelled orders in the `benchmark-ecommerce` index are Hans Mueller, Elena Moreno Jiménez, and Lucía Romero Gutiérrez (in that order). ✓ Fully Supported⚓ Grounded Matches the ground truth ordering and is directly supported by the ES|QL results sorted by total_revenue DESC with LIMIT 3. ●Hans Mueller has total revenue 68,600.24 and 55 orders from non-cancelled orders. ✓ Fully Supported⚓ Grounded Ground truth states €68,600.24 and 55 orders; tool output shows 68600.240669... and 55, which rounds to 68,600.24. ●Hans Mueller’s average order value is 1,247.28 (from non-cancelled orders). ✓ Fully Supported⚓ Grounded Ground truth avg is €1,247.28; tool output shows 1247.277103..., which rounds to 1,247.28. ●Elena Moreno Jiménez has total revenue 55,882.65, 47 orders, and an average order value of 1,188.99 (from non-cancelled orders). ✓ Fully Supported⚓ Grounded All three values match the ground truth; tool output contains 55882.650264..., 47, and 1188.992558..., rounding to the reported figures. ●Lucía Romero Gutiérrez has total revenue 52,188.99, 43 orders, and an average order value of 1,213.70 (from non-cancelled orders). ✓ Partially Supported⚓ Grounded Order count (43) and avg (≈1213.70) match ground truth/tool output; total revenue is slightly off vs ground truth (€52,189.00) due to rounding/truncation (tool output shows 52188.999507...). ○Hans Mueller leads with the highest total revenue and order count among these top 3 customers. ✓ Fully Supported⚓ Grounded Within the returned top-3 rows, Hans has the largest total_revenue and the largest order_count per the tool output and ground truth. ○The monetary values are denominated in dollars (e.g., "$68,600.24"). ✓ Contradicted⚓ Ungrounded [minor] Ground truth expresses amounts in euros (€), and the tool output provides numeric values with no currency symbol; presenting them as dollars is unsupported and conflicts with the expected currency context. 🧑⚖️ Judge ReasoningThe agent correctly identifies the top 3 customers and reports revenue, order counts, and average order values consistent with the tool output, with only minor rounding differences for Lucía’s total. The main issue is the unsupported use of a dollar currency symbol, which contradicts the euro-denominated ground truth context. Otherwise, the response follows instructions and formats the results as a table.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.0 Groundedness 9.0 Relevance 7.1 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 3.2 Cost 6.8 Err Rate 10.0 Overall 8.5 💬 Respuesta del agente (extracto)| Customer Name | Total Revenue | Number of Orders | Average Order Value |
|---------------|---------------|------------------|---------------------|
| Hans Mueller | $68,600.24 | 55 | $1,247.28 |
| Elena Moreno Jiménez | $55,882.65 | 47 | $1,188.99 |
| Lucía Romero Gutiérrez | $52,188.99 | 43 | $1,213.70 |
These are the top 3 customers by total revenue from non-cancelled orders in the `benchmark-ecommerce` index. Hans Mueller leads with the highest total revenue and order count, while maintaining a strong average order value across all 55 orders.
| ||||||||||||
| ❌ expert_02 🔗 | 💬 expert | expert | Compare revenue in Q3 (July-September) vs Q4 … | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaCompare revenue in Q3 (July-September) vs Q4 (October-December) 2025 by product category in 'benchmark-ecommerce'. Which category grew the most between Q3 and Q4? Which declined the most?
🎯 GROUND TRUTH:
The agent should calculate revenue per category for Q3 (Jul-Sep) and Q4 (Oct-Dec) separately. Exact Q3→Q4 changes: books grew +35.9% (€6,656→€9,047, most growth), electronics declined -18.4% (€377,716→€308,321), clothing declined -10.0% (€23,738→€21,366), sports declined -32.1% (€12,095→€8,208, most decline in percentage), bags declined -25.5% (€9,194→€6,845), home was flat -0.7%. Tests temporal analysis with percentage calculations. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ expert_03 🔗 | 🔄 expert | expert | Show me the mapping of 'benchmark-ecommerce' … | 22.3s | $0.0931 | 9.8 | 8.2 | 10.0 | 7.0 | 8.30 | 19 3? | |
❓ Conversación (5 turns)Turn 1: Show me the mapping of 'benchmark-ecommerce' index Turn 2: What are the different order statuses and their counts? Turn 3: Now focus on the cancelled orders — what is the most common payment method among cancelled orders? Turn 4: Interesting. Now compare: is the cancellation rate higher for orders placed in the first 3 months (July-September) vs the last 3 months (October-December)? Turn 5: Summarize everything we've found in this conversation about cancellations — patterns, payment methods, timing. Be concise. 🎯 GROUND TRUTH:
The agent should: 1) Show mapping, 2) Count by status: delivered (522), shipped (123), cancelled (111), processing (97), pending (95), returned (52), 3) Most common payment method for cancelled orders is credit_card (38), followed by paypal (29) and debit_card (26), 4) Cancellation rate Q3 (Jul-Sep): 47/496 = 9.5%, Q4 (Oct-Dec): 64/504 = 12.7% — Q4 has a higher cancellation rate, 5) Provide a coherent summary. Tests deep multi-turn with 5 turns and context retention. 📋 Claim Decomposition📋 Claim Analysis
19 claims (8 central) · 3 ungrounded ●There are 111 cancelled orders out of 1,000 total orders (11.1%). ✓ Fully Supported⚓ Grounded Matches the status counts (111 cancelled out of 1000) shown in tool output/ground truth. ○Cancelled is the third most common status after delivered (52.2%) and shipped (12.3%). ✓ Fully Supported⚓ Grounded Tool output shows delivered 522, shipped 123, cancelled 111; percentages align with /1000. ●Among cancelled orders, credit_card has 38 cancellations (34.2%). ✓ Fully Supported⚓ Grounded Tool output shows 38 credit_card cancelled; 38/111=34.2%. ●Among cancelled orders, paypal has 29 cancellations (26.1%). ✓ Fully Supported⚓ Grounded Tool output shows 29 paypal cancelled; 29/111=26.1%. ●Among cancelled orders, debit_card has 26 cancellations (23.4%). ✓ Fully Supported⚓ Grounded Tool output shows 26 debit_card cancelled; 26/111=23.4%. ○Among cancelled orders, bank_transfer has 10 cancellations (9.0%). ✓ Fully Supported⚓ Grounded Tool output shows 10 bank_transfer cancelled; 10/111=9.0%. ○Among cancelled orders, cash_on_delivery has 8 cancellations (7.2%). ✓ Fully Supported⚓ Grounded Tool output shows 8 cash_on_delivery cancelled; 8/111=7.2%. ●Card payments (credit + debit) account for 57.7% of cancellations. ✓ Fully Supported⚓ Grounded (38+26)/111=64/111=57.7%, derived directly from tool counts. ○Traditional methods (bank transfer + cash) represent 16.2% of cancellations. ✓ Fully Supported⚓ Grounded (10+8)/111=18/111=16.2%, derived directly from tool counts. ●From July–September there were 496 total orders and 47 cancelled orders (9.5% cancellation rate). ✓ Fully Supported⚓ Partially Grounded Monthly tool data sums to 496 total and 47 cancelled; the 9.5% rate is a correct calculation from those counts, though the tool output did not directly provide the aggregated period row. ●From October–December there were 504 total orders and 64 cancelled orders (12.7% cancellation rate). ✓ Fully Supported⚓ Partially Grounded Monthly tool data sums to 504 total and 64 cancelled; 12.7% is a correct calculation from those counts, though the tool output did not directly provide the aggregated period row. ○Cancellation rate increased 33.7% in Oct–Dec compared to Jul–Sep. ✓ Fully Supported⚓ Partially Grounded Computed from the stated rates: (12.7%-9.5%)/9.5%=33.7%; rates are supported by counts. ○Jul–Sep cancellation rates were stable at roughly 9.2% to 9.9% by month. ✓ Fully Supported⚓ Partially Grounded Derivable from tool counts: Jul 16/174=9.2%, Aug 16/171=9.4%, Sep 15/151=9.9%. ○Oct–Dec cancellation rates were more volatile, ranging roughly 10.8% to 15.8% by month. ✓ Fully Supported⚓ Partially Grounded Derivable from tool counts: Oct 27/171=15.8%, Nov 17/158=10.8%, Dec 20/175=11.4%. ○October’s cancellation rate was 15.8%, the highest of any month (Jul–Dec). ✓ Fully Supported⚓ Partially Grounded From tool counts, Oct 27/171=15.8% and exceeds Jul, Aug, Sep, Nov, Dec. … y 4 más 🧑⚖️ Judge ReasoningThe final response correctly retains and summarizes key cancellation facts across turns (overall cancelled volume, payment-method distribution, and Jul–Sep vs Oct–Dec comparison) and most quantitative statements are supported by tool outputs or are valid calculations from them. However, it introduces at least one unsupported causal/relative-risk claim ("3.8x more likely") and several speculative recommendations beyond the user’s request for a concise summary.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 9.8 Groundedness 8.2 Relevance 4.2 Format 10.0 Tool Calling 10.0 Instruct. 7.0 Latency 6.3 Cost 3.5 Err Rate 10.0 Overall 8.3 💬 Respuesta del agente (extracto)# Cancellation Analysis Summary
## Overall Volume
- **111 cancelled orders** out of 1,000 total (**11.1%** of all orders)
- Third most common status after delivered (52.2%) and shipped (12.3%)
---
## Payment Method Patterns
| Payment Method | Cancelled Orders | % of Cancellations |
|----------------|------------------|-------------------|
| credit_card | 38 | 34.2% |
| paypal | 29 | 26.1% |
| debit_card | 26 | 23.4% |
| bank_transfer | 10 | 9.0% |
| cash_on_delivery | 8 | 7.2% |
**Key Finding:** Card payments (credit + debit) account for **57.7%** of cancellations, while traditional metho
| ||||||||||||
| ✅ format_01 🔗 | 💬 hard | format_strict | Give me exactly 3 cancelled orders from 'benc… | 95.0s | $0.0354 | 10.0 | 10.0 | 10.0 | 10.0 | 8.91 | 5 | |
❓ PreguntaGive me exactly 3 cancelled orders from 'benchmark-ecommerce' as a JSON array. ONLY output the JSON array, nothing else. No markdown, no explanation, no code blocks. Each object should have order_id, customer name, and total_amount.
🎯 GROUND TRUTH:
The agent should search for cancelled orders, pick 3, and output ONLY a JSON array with the specified fields. No markdown formatting, no explanation text. Tests strict format adherence. 📋 Claim Decomposition📋 Claim Analysis
5 claims (5 central) · all grounded ●The response outputs exactly 3 cancelled orders from the 'benchmark-ecommerce' index. ✓ Fully Supported⚓ Grounded The tool query filters WHERE status == "cancelled" and the response includes exactly three entries drawn from the returned cancelled-orders rows. ●Order ORD-2025-00014 has customer name John Smith and total_amount 2841.47998046875. ✓ Fully Supported⚓ Grounded This exact tuple (order_id, customer.name, total_amount) appears in the tool output values. ●Order ORD-2025-00020 has customer name Pedro Sánchez Díaz and total_amount 899.7100219726562. ✓ Fully Supported⚓ Grounded This exact tuple appears in the tool output values. ●Order ORD-2025-00022 has customer name Pedro Sánchez Díaz and total_amount 321.239990234375. ✓ Fully Supported⚓ Grounded This exact tuple appears in the tool output values. ●The response is a JSON array containing objects with the fields order_id, customer_name, and total_amount, and contains no additional text. ✓ Fully Supported⚓ Grounded The agent response is only a JSON array and each element includes the requested fields; the values are consistent with the tool output (customer.name mapped to customer_name). 🧑⚖️ Judge ReasoningThe agent returned exactly three results and used only a JSON array with the requested fields and no extra text, matching the strict formatting requirement. All provided order_id, customer_name, and total_amount values directly match rows shown in the tool output for cancelled orders.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 1.0 Cost 6.2 Err Rate 10.0 Overall 8.9 💬 Respuesta del agente (extracto)[{"order_id":"ORD-2025-00014","customer_name":"John Smith","total_amount":2841.47998046875},{"order_id":"ORD-2025-00020","customer_name":"Pedro Sánchez Díaz","total_amount":899.7100219726562},{"order_id":"ORD-2025-00022","customer_name":"Pedro Sánchez Díaz","total_amount":321.239990234375}]
| ||||||||||||
| ✅ format_02 🔗 | 💬 medium | format_strict | Answer with ONLY a number, nothing else: How … | 53.1s | $0.0202 | 9.3 | 8.2 | 10.0 | 1.0 | 7.36 | 5 1✗ 1? | |
❓ PreguntaAnswer with ONLY a number, nothing else: How many orders in 'benchmark-ecommerce' have status 'pending'?
🎯 GROUND TRUTH:
The exact answer is 95. The agent should respond with ONLY the number '95' — no words, no explanation, no formatting. Tests extreme instruction following. 📋 Claim Decomposition📋 Claim Analysis
5 claims (1 central) · 1 contradicted · 1 ungrounded ●The count of orders in the 'benchmark-ecommerce' index with status "pending" is 95. ✓ Fully Supported⚓ Grounded Matches the ground truth exact answer (95) and is directly supported by the tool output value [[95]]. ○A query `FROM benchmark-ecommerce | WHERE status == "pending" | STATS COUNT(*)` was executed and returned 95. ✓ Fully Supported⚓ Grounded The tool output explicitly shows the ES|QL query and the resulting count of 95. ○The user explicitly demanded the response contain ONLY a number and nothing else. ✓ Fully Supported⚓ Disclosed Ungrounded This is accurate per the user query text, but it is not derived from the tool output (it comes from the prompt). ○The agent's output should be strictly `95` with no additional text, markdown, explanations, or context. ✓ Fully Supported⚓ Disclosed Ungrounded This aligns with the user’s instruction, but it is a meta-statement about formatting, not supported by tool output. ○No internal tool process or names were revealed in the response. ✓ Contradicted⚓ Ungrounded [minor] The response reveals internal reasoning/process details including the executed query and analysis steps; this is contrary to the claim and not supported by tool output. 🧑⚖️ Judge ReasoningThe central factual answer (95 pending orders) is correct and grounded in the tool output. However, the agent severely violated the user’s instruction to respond with ONLY a number by including extensive reasoning and extra text, leading to very low instruction-following and formatting scores.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 95FOUND: 95📊 Todas las puntuacionesCorrectness 9.3 Groundedness 8.2 Relevance 2.0 Format 2.0 Tool Calling 10.0 Instruct. 1.0 Latency 3.5 Cost 7.0 Err Rate 10.0 Overall 7.4 💬 Respuesta del agente (extracto)Thinking Process:
1. **Analyze the Request:**
* User Question: "How many orders in 'benchmark-ecommerce' have status 'pending'?"
* Constraint: "Answer with ONLY a number, nothing else".
* Role: Final answering agent (Elastic AI assistant).
* Input Data: Research results show a query executed (`FROM benchmark-ecommerce | WHERE status == "pending" | STATS COUNT(*)`) returned a value of `95`.
2. **Verify Information:**
* Index: `benchmark-ecommerce`
* Condition: `status == "pending"`
* Result: `95`
* Constraint Check: The user explicitly demande
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| ✅ instruction_01 🔗 | 💬 medium | instruction_following | List all unique product categories in the 'be… | 60.9s | $0.0214 | 9.4 | 6.2 | 10.0 | 9.0 | 7.96 | 8 3? | |
❓ PreguntaList all unique product categories in the 'benchmark-ecommerce' index. Format your response as a markdown table with columns: Category, Description (infer from category name).
🎯 GROUND TRUTH:
The agent should find the 6 unique product categories: electronics (413 orders), home (203), clothing (186), books (104), bags (52), sports (42) — and present them in a markdown table with columns Category and Description. Tests instruction following for specific output format. 📋 Claim Decomposition📋 Claim Analysis
8 claims (5 central) · 3 ungrounded ●The 'benchmark-ecommerce' index contains 6 unique product categories. ✓ Fully Supported⚓ Grounded Ground truth expects 6 unique categories, and the tool output lists exactly six distinct product.category values. ●The unique product categories are: bags, books, clothing, electronics, home, and sports. ✓ Fully Supported⚓ Grounded These category names exactly match both the ground truth list and the categories returned in the tool output. ○There are 1,000 total products distributed across the categories. ✓ Fully Supported⚓ Grounded Summing tool counts (52+104+186+413+203+42) equals 1,000; this aligns with the distribution implied by the ground truth counts. ○Electronics is the largest category with 413 products. ✓ Fully Supported⚓ Grounded Tool output shows electronics count = 413, which is also the largest and matches ground truth. ○Home is the second-largest category with 203 products, followed by clothing with 186 products. ✓ Fully Supported⚓ Grounded Tool output shows home=203 and clothing=186, which matches the ground truth ordering and values. ●Bags refers to handbags, backpacks, luggage, and other carrying accessories. ✓ Not Verifiable⚓ Ungrounded [minor] The tool output and ground truth provide only category names and counts, not semantic definitions; this description is a plausible inference but not supported by provided data. ●Books refers to printed and digital books across various genres and topics. ✓ Not Verifiable⚓ Ungrounded [minor] No descriptions exist in tool output or ground truth; the statement is interpretive rather than evidenced. ●Clothing refers to apparel and garments for men, women, and children; electronics refers to consumer electronic devices; home refers to furniture/decor; sports refers to sporting goods. ✓ Not Verifiable⚓ Ungrounded [minor] These are inferred meanings of the category labels and are not provided or confirmable from the tool output/ground truth beyond the labels themselves. 🧑⚖️ Judge ReasoningThe agent correctly identified all six unique categories and accurately summarized category dominance using counts that are fully supported by the tool output and match the ground truth. The markdown table format and required columns were followed. The only ungrounded elements are the category descriptions, which are reasonable inferences but not supported by the provided data.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.4 Groundedness 6.2 Relevance 6.2 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 2.9 Cost 6.9 Err Rate 10.0 Overall 8.0 💬 Respuesta del agente (extracto)| Category | Description |
|----------|-------------|
| bags | Handbags, backpacks, luggage, and other carrying accessories |
| books | Printed and digital books across various genres and topics |
| clothing | Apparel and garments for men, women, and children |
| electronics | Consumer electronic devices, gadgets, and accessories |
| home | Furniture, decor, and household items for living spaces |
| sports | Sporting goods, equipment, and athletic accessories |
The index contains **6 unique product categories** with a total of 1,000 products distributed across them. Electronics is the largest
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| ✅ instruction_02 🔗 | 💬 medium | instruction_following | In exactly 3 bullet points, summarize what th… | 85.3s | $0.0495 | 8.2 | 9.0 | 10.0 | 6.0 | 7.78 | 7 | |
❓ PreguntaIn exactly 3 bullet points, summarize what the 'benchmark-ecommerce' index contains. Use the mapping and a quick search to understand the data.
🎯 GROUND TRUTH:
The agent should examine the mapping and/or data, then respond with exactly 3 bullet points summarizing the ecommerce orders data. The index contains 1000 e-commerce orders from 20 customers across 6 product categories (electronics, home, clothing, books, bags, sports) with 6 statuses (delivered, shipped, cancelled, processing, pending, returned). Tests adherence to response format constraints — must be exactly 3 bullet points. 📋 Claim Decomposition📋 Claim Analysis
7 claims (6 central) · all grounded ●The index contains customer profile fields including name, email, city, and country. ✓ Not Verifiable⚓ Grounded Ground truth focuses on dataset size/customers/categories/statuses rather than specific field names, so correctness can’t be confirmed from it. The mapping and sample columns explicitly include customer.name, customer.email, customer.city, and customer.country. ●The index contains order identifiers and key dates such as order date, estimated delivery date, and delivered-at date. ✓ Not Verifiable⚓ Grounded Ground truth does not enumerate these fields. Tool output shows order_id, order_date, estimated_delivery, and delivered_at in the mapping and the sample search columns. ●Order status tracking includes values like pending, shipped, delivered, cancelled, and returned. ✓ Partially Supported⚓ Partially Grounded Ground truth lists six statuses (delivered, shipped, cancelled, processing, pending, returned); the claim matches most but omits 'processing'. Tool output confirms a status field and shows at least 'delivered' in the sample, but does not fully enumerate all listed statuses. ●The index includes priority, payment method, gift flag, and delivery notes fields. ✓ Not Verifiable⚓ Grounded Ground truth does not specify these fields. Tool output mapping includes priority, payment_method, is_gift, and notes, and the sample shows corresponding columns/values. ●The index includes product information (name, category, subcategory) and categories such as electronics, clothing, books, and home. ✓ Partially Supported⚓ Partially Grounded Ground truth categories are electronics, home, clothing, books, bags, sports; the claim lists some correct categories but omits bags and sports (and uses 'etc.'). Tool output confirms product.name/category/subcategory fields; the visible sample shows 'electronics' but not all the listed categories. ●The index contains transaction/financial fields including unit price, quantity, total amount, tax amount, shipping cost, discount percentage, and customer ratings. ✓ Not Verifiable⚓ Grounded Ground truth doesn’t mention these specific fields. Tool output mapping explicitly includes unit_price, quantity, total_amount, tax_amount, shipping_cost, discount_pct, and rating. ○Shipping methods include standard, express, next_day, and pickup, and tags categorize orders with values such as gift, promotional, vip, wholesale, international, fragile, rush, seasonal, new_customer, and returning_customer. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not specify shipping methods or tag vocabularies. Tool output confirms shipping_method and tags fields exist and shows at least shipping_method='standard' and tags including 'international', 'new_customer', and 'wholesale'; the additional listed values are plausible but not directly evidenced in the visible tool excerpt. 🧑⚖️ Judge ReasoningThe response uses exactly three bullet points and accurately reflects many mapped fields (customer, order, product, and financial attributes), so it is largely grounded in the tool output. However, it does not include key expected dataset facts from the ground truth (e.g., 1000 orders, 20 customers, full set of 6 categories and 6 statuses), so instruction-following is only partial.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.2 Groundedness 9.0 Relevance 8.6 Format 10.0 Tool Calling 10.0 Instruct. 6.0 Latency 1.3 Cost 5.5 Err Rate 10.0 Overall 7.8 💬 Respuesta del agente (extracto)- **Customer and order metadata**: Complete customer profiles (name, email, city, country) paired with order identifiers, dates (order, estimated delivery, delivered), status tracking (pending, shipped, delivered, cancelled, returned), priority levels, payment methods, and gift flags with delivery notes.
- **Product and financial details**: Product information including name, category, and subcategory (electronics, clothing, books, home, etc.), along with transaction data such as unit price, quantity, total amount, tax amount, shipping cost, discount percentages, and customer ratings (1-5 sca
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| ✅ multiturn_01 🔗 | 🔄 hard | multi_turn | Show me the mapping of the 'benchmark-ecommer… | 62.1s | $0.0498 | 8.7 | 8.8 | 10.0 | 10.0 | 8.39 | 5 | |
❓ Conversación (3 turns)Turn 1: Show me the mapping of the 'benchmark-ecommerce' index Turn 2: Based on that mapping, how many unique product categories are there? Use a query to find out. Turn 3: Now show me the total revenue for the top category you found. 🎯 GROUND TRUTH:
The agent should: 1) Show the mapping (fields include product.category, total_amount, etc.), 2) Find there are exactly 6 unique product categories (electronics, home, clothing, books, bags, sports), 3) Remember that electronics is the top category and calculate its total revenue: €686,037.84 from 413 orders. Tests context retention across 3 turns. 📋 Claim Decomposition📋 Claim Analysis
5 claims (4 central) · all grounded ●The top product category is electronics. ✓ Fully Supported⚓ Grounded Tool output for revenue by category shows the highest SUM(total_amount) is for "electronics". ●Electronics has a total revenue of $686,037.84. ✓ Partially Supported⚓ Partially Grounded The tool output supports the numeric revenue value (686037.8392 ≈ 686,037.84), but it does not support the currency symbol "$" (ground truth expects €). ○This represents approximately 74% of the total revenue across all 6 categories in the benchmark-ecommerce index. ✓ Fully Supported⚓ Grounded Tool output shows 6 categories, and electronics revenue divided by the sum of all listed category revenues is ~74.6%, which matches "approximately 74%". ●Revenue by category: electronics $686,037.84; home $137,010.55; clothing $45,103.57; sports $20,303.18; bags $16,039.01; books $15,702.88. ✓ Partially Supported⚓ Partially Grounded All category names and numeric values match the tool output when rounded to 2 decimals, but the "$" currency symbol is not supported by the tool output (and expected currency is €). ●The query used to calculate this revenue was: FROM benchmark-ecommerce | STATS revenue = SUM(total_amount) BY product.category | SORT revenue DESC. ✓ Fully Supported⚓ Grounded This matches the tool-provided ESQL structure (the tool also included LIMIT 100, but omission does not change the core calculation). 🧑⚖️ Judge ReasoningThe agent correctly retained that there are 6 categories and that electronics is the top category, and it used the appropriate field (total_amount) to compute revenue by product.category. The numeric results and query are well grounded in tool output, but the response introduces an unsupported currency symbol ("$") where the expected behavior indicates euros.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.7 Groundedness 8.8 Relevance 8.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 2.9 Cost 5.5 Err Rate 10.0 Overall 8.4 💬 Respuesta del agente (extracto)The top product category is **electronics** with a total revenue of **$686,037.84**.
This represents approximately 74% of the total revenue across all 6 categories in the `benchmark-ecommerce` index.
## Revenue by Category (All)
| Category | Total Revenue |
|----------|---------------|
| **electronics** | **$686,037.84** |
| home | $137,010.55 |
| clothing | $45,103.57 |
| sports | $20,303.18 |
| bags | $16,039.01 |
| books | $15,702.88 |
The query used to calculate this:
```esql
FROM benchmark-ecommerce
| STATS revenue = SUM(total_amount) BY product.category
| SORT revenue DESC
```
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| ✅ multiturn_02 🔗 | 🔄 hard | multi_turn | Search for orders in the 'benchmark-ecommerce… | 90.8s | $0.0717 | 9.6 | 9.3 | 10.0 | 9.0 | 8.05 | 22 1? | |
❓ Conversación (2 turns)Turn 1: Search for orders in the 'benchmark-ecommerce' index from customers in Spain Turn 2: Now narrow it down: from those Spanish customers, show me only the cancelled orders with high priority 🎯 GROUND TRUTH:
The agent should first search for Spanish customers (736 orders from Spain), then refine the search adding status=cancelled and priority=high (exactly 15 orders match all three conditions: country=Spain, status=cancelled, priority=high). Tests progressive query refinement across 2 turns. 📋 Claim Decomposition📋 Claim Analysis
22 claims (3 central) · 1 ungrounded ●There are 15 orders in `benchmark-ecommerce` from Spanish customers with status=cancelled and priority=high. ✓ Fully Supported⚓ Grounded Matches ground truth (exactly 15) and the tool query/output returns 15 matching rows (LIMIT 100, 15 values shown/returned). ○Order ORD-2025-00022 is a cancelled, high-priority order from Spain for Pedro Sánchez Díaz (pedro.sanchez@email.com) with total amount €321.24 on 2025-08-28. ✓ Not Verifiable⚓ Grounded This exact row is present in the tool output with matching order_id, name, email, amount (321.23999...), and date (2025-08-28...). ○Order ORD-2025-00037 is a cancelled, high-priority order from Spain for María García López (maria.garcia@email.com) with total amount €1,059.61 on 2025-12-27. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (1059.60998..., 2025-12-27...). ○Order ORD-2025-00159 is a cancelled, high-priority order from Spain for Elena Moreno Jiménez (elena.moreno@email.com) with total amount €142.00 on 2025-10-04. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (amount 142, date 2025-10-04...). ○Order ORD-2025-00361 is a cancelled, high-priority order from Spain for Ana Fernández Ruiz (ana.fernandez@email.com) with total amount €2,012.78 on 2025-09-09. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (2012.7800..., 2025-09-09...). ○Order ORD-2025-00408 is a cancelled, high-priority order from Spain for Andrés Gómez Castro (andres.gomez@email.com) with total amount €182.54 on 2025-12-06. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (182.53999..., 2025-12-06...). ○Order ORD-2025-00426 is a cancelled, high-priority order from Spain for Sofia Torres Navarro (sofia.torres@email.com) with total amount €629.44 on 2025-12-20. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (629.4400..., 2025-12-20...). ○Order ORD-2025-00433 is a cancelled, high-priority order from Spain for Carlos Rodríguez Martín (carlos.rodriguez@email.com) with total amount €706.36 on 2025-12-22. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (706.35998..., 2025-12-22...). ○Order ORD-2025-00513 is a cancelled, high-priority order from Spain for Javier Ruiz Hernández (javier.ruiz@email.com) with total amount €3,602.81 on 2025-11-18. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (3602.8100..., 2025-11-18...). ○Order ORD-2025-00524 is a cancelled, high-priority order from Spain for Ana Fernández Ruiz (ana.fernandez@email.com) with total amount €171.86 on 2025-08-26. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (171.8600..., 2025-08-26...). ○Order ORD-2025-00545 is a cancelled, high-priority order from Spain for Carlos Rodríguez Martín (carlos.rodriguez@email.com) with total amount €169.04 on 2025-08-23. ✓ Not Verifiable⚓ Grounded This row is present in the tool output with matching fields (169.03999..., 2025-08-23...). ○Order ORD-2025-00568 is a cancelled, high-priority order from Spain for Francisco Álvarez Morales (francisco.alvarez@email.com) with total amount €429.24 on 2025-10-14. ✓ Not Verifiable⚓ Partially Grounded The tool output is truncated starting at the ORD-2025-00568 row; the full row details are not visible, so this cannot be fully verified from the provided tool excerpt. ○Order ORD-2025-00612 is a cancelled, high-priority order from Spain for Miguel Hernández Romero (miguel.hernandez@email.com) with total amount €409.08 on 2025-10-22. ✓ Not Verifiable⚓ Partially Grounded This row is not visible in the provided (truncated) tool output excerpt, so it cannot be confirmed here; truncation prevents verification. ○Order ORD-2025-00622 is a cancelled, high-priority order from Spain for Javier Ruiz Hernández (javier.ruiz@email.com) with total amount €2,443.70 on 2025-09-03. ✓ Not Verifiable⚓ Partially Grounded Not visible in the provided (truncated) tool output excerpt; truncation prevents verification. ○Order ORD-2025-00643 is a cancelled, high-priority order from Spain for Miguel Hernández Romero (miguel.hernandez@email.com) with total amount €737.30 on 2025-10-05. ✓ Not Verifiable⚓ Partially Grounded Not visible in the provided (truncated) tool output excerpt; truncation prevents verification. … y 7 más 🧑⚖️ Judge ReasoningThe agent correctly retained the prior context (Spain customers) and refined the query to status=cancelled and priority=high, matching the expected 15 results. Most row-level details are grounded in the tool output, with some unverifiable only due to truncation. The main issue is an ungrounded and likely incorrect aggregate 'Total Value' computation.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.6 Groundedness 9.3 Relevance 1.4 Format 10.0 Tool Calling 10.0 Instruct. 9.0 Latency 1.0 Cost 4.4 Err Rate 10.0 Overall 8.1 💬 Respuesta del agente (extracto)Found **15 cancelled orders with high priority** from Spanish customers in the `benchmark-ecommerce` index.
## High Priority Cancelled Orders (Spain)
| Order ID | Customer | Email | Total Amount | Order Date |
|----------|----------|-------|--------------|------------|
| ORD-2025-00022 | Pedro Sánchez Díaz | pedro.sanchez@email.com | €321.24 | 2025-08-28 |
| ORD-2025-00037 | María García López | maria.garcia@email.com | €1,059.61 | 2025-12-27 |
| ORD-2025-00159 | Elena Moreno Jiménez | elena.moreno@email.com | €142.00 | 2025-10-04 |
| ORD-2025-00361 | Ana Fernández Ruiz | ana.fernandez@email
| ||||||||||||
| ✅ multiturn_03 🔗 | 🔄 medium | multi_turn | Count the orders in the 'benchmark-ecomerce' … | 10.2s | $0.0760 | 10.0 | 10.0 | 10.0 | 10.0 | 9.25 | 1 | |
❓ Conversación (2 turns)Turn 1: Count the orders in the 'benchmark-ecomerce' index Turn 2: Sorry, I misspelled it. The correct index name is 'benchmark-ecommerce' (with double m). Try again. 🎯 GROUND TRUTH:
The agent should handle the user's typo correction gracefully. First turn may error, second turn should use the corrected name and return count of 1000. Tests error recovery. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●The `benchmark-ecommerce` index contains 1,000 orders. ✓ Fully Supported⚓ Grounded Ground truth expects a count of 1000 for `benchmark-ecommerce`, and the tool output shows an ES|QL COUNT(*) result of 1000 for that index. 🧑⚖️ Judge ReasoningThe final response contains a single central factual claim: the document/order count in `benchmark-ecommerce`. This matches the expected behavior and is directly supported by the tool’s COUNT(*) output. The agent also correctly used the user-corrected index name, demonstrating strong context retention.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.list_indices
USED: platform.core.search platform.core.list_indices
🎯 Exact Answer Check ✅EXPECTED: 1000FOUND: 1000📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 8.4 Cost 4.2 Err Rate 7.0 Overall 9.2 💬 Respuesta del agente (extracto)The `benchmark-ecommerce` index contains **1,000 orders**.
| ||||||||||||
| ✅ reasoning_01 🔗 | 💬 hard | reasoning | First, show me the mapping of the 'benchmark-… | 60.8s | $0.0252 | 8.6 | 10.0 | 10.0 | 6.0 | 8.23 | 7 | |
❓ PreguntaFirst, show me the mapping of the 'benchmark-ecommerce' index. Then, based on the mapping, write a query to find the top 3 customers by number of orders.
🎯 GROUND TRUTH:
The agent should first get the mapping to understand the schema, then query to group by customer.name.keyword, count orders, and sort descending limited to 3. The top 3 customers by order count are: Hans Mueller (59 orders), John Smith (59 orders), and Isabel Jiménez Ortega (58 orders). This requires multi-step reasoning using mapping info. 📋 Claim Decomposition📋 Claim Analysis
7 claims (5 central) · all grounded ○In the benchmark-ecommerce index mapping, `customer.email` is of type `keyword`. ✓ Fully Supported⚓ Grounded Tool mapping explicitly shows `customer.email` as `keyword`, matching the agent’s mapping summary. ●In the benchmark-ecommerce index mapping, `customer.name` is `text` with a `keyword` subfield. ✓ Fully Supported⚓ Grounded Tool mapping shows `customer.name` as `text` with `fields.keyword` of type `keyword`, matching the agent. ○In the benchmark-ecommerce index mapping, fields like `order_id` (keyword), `order_date` (date), `status` (keyword), `product.name` (text with keyword subfield), `quantity` (integer), `total_amount` (float), `payment_method` (keyword), `shipping_method` (keyword), `delivered_at` (date), and `is_gift` (boolean) exist with those types. ✓ Fully Supported⚓ Grounded All listed fields and types appear in the tool mapping output under `properties`. ●An ES|QL query to find the top 3 customers by number of orders is: `FROM benchmark-ecommerce | STATS order_count = COUNT(*) BY customer.email | SORT order_count DESC | LIMIT 3`. ✓ Partially Supported⚓ Grounded The query exactly matches the tool search output and does produce top-3 by order count, but the ground truth expects grouping by `customer.name.keyword` (customer name), not by email. ●The top 3 results by order count are: john.smith@email.com (59), hans.mueller@email.com (59), and isabel.jimenez@email.com (58). ✓ Partially Supported⚓ Grounded These exact emails and counts are in the tool tabular output; however, ground truth expects the top 3 customers to be reported by name (Hans Mueller, John Smith, Isabel Jiménez Ortega) rather than email. ●john.smith@email.com and hans.mueller@email.com are tied for first place with 59 orders each. ✓ Fully Supported⚓ Grounded Tool output shows both emails with order_count 59, supporting the tie statement. ●The query aggregates orders by `customer.email`, counts them, sorts in descending order, and returns the top 3. ✓ Partially Supported⚓ Grounded This is an accurate description of the provided ES|QL query and tool output, but it does not follow the ground-truth expectation to aggregate by customer name (`customer.name.keyword`) to answer 'top customers' as specified. 🧑⚖️ Judge ReasoningThe agent’s mapping summary and ES|QL query are grounded in the tool outputs, and the reported top-3 emails/counts match the retrieved results. However, relative to the expected solution, it should have used the mapping to aggregate by `customer.name.keyword` and present the top customers by name, not by email, so the core query choice is only partially correct for the stated requirement.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 8.6 Groundedness 10.0 Relevance 7.1 Format 9.0 Tool Calling 10.0 Instruct. 6.0 Latency 2.9 Cost 6.7 Err Rate 10.0 Overall 8.2 💬 Respuesta del agente (extracto)## Index Mapping: benchmark-ecommerce
The index contains the following key fields:
| Field | Type |
|-------|------|
| `customer.email` | keyword |
| `customer.name` | text (with keyword subfield) |
| `customer.city` | keyword |
| `customer.country` | keyword |
| `order_id` | keyword |
| `order_date` | date |
| `status` | keyword |
| `product.name` | text (with keyword subfield) |
| `product.category` | keyword |
| `quantity` | integer |
| `total_amount` | float |
| `payment_method` | keyword |
| `shipping_method` | keyword |
| `delivered_at` | date |
| `is_gift` | boolean |
## Top 3 Custom
| ||||||||||||
| ✅ reasoning_02 🔗 | 💬 medium | reasoning | Analyze the 'benchmark-ecommerce' index: how … | 55.7s | $0.0207 | 9.9 | 9.9 | 10.0 | 10.0 | 8.79 | 9 | |
❓ PreguntaAnalyze the 'benchmark-ecommerce' index: how many orders are there per status? Which status has the most orders? Give me a summary.
🎯 GROUND TRUTH:
The agent should group by status and count orders. The exact distribution is: delivered (522), shipped (123), cancelled (111), processing (97), pending (95), returned (52). 'delivered' has the most orders with 522. The agent should provide a clear summary interpreting these results. 📋 Claim Decomposition📋 Claim Analysis
9 claims (3 central) · all grounded ●The number of orders per status is: delivered 522, shipped 123, cancelled 111, processing 97, pending 95, returned 52. ✓ Fully Supported⚓ Grounded Matches the Ground Truth distribution exactly and is directly supported by the tool output table values. ●There are 1,000 total orders in the 'benchmark-ecommerce' index (sum of all statuses). ✓ Fully Supported⚓ Grounded The Ground Truth counts sum to 1,000, and the tool output counts also sum to 1,000. ●The status with the most orders is delivered with 522 orders. ✓ Fully Supported⚓ Grounded Ground Truth explicitly states delivered has the most with 522; tool output shows delivered 522 which is the maximum. ○The least common status is returned with 52 orders (5.2% of all orders). ✓ Fully Supported⚓ Grounded Returned has the smallest count (52) per Ground Truth/tool output; 52/1000 = 5.2% is correct. ○Delivered accounts for 52.2% of all orders. ✓ Fully Supported⚓ Grounded Computed from grounded counts: 522 out of 1,000 equals 52.2%. ○Completed orders (delivered + shipped + returned) total 697 orders (69.7%). ✓ Fully Supported⚓ Grounded Sum of grounded counts: 522 + 123 + 52 = 697; 697/1000 = 69.7%. ○In-progress orders (pending + processing) total 192 orders (19.2%). ✓ Fully Supported⚓ Grounded Sum of grounded counts: 95 + 97 = 192; 192/1000 = 19.2%. ○Cancelled orders total 111 orders (11.1%). ✓ Fully Supported⚓ Grounded Cancelled count (111) matches Ground Truth/tool output; 111/1000 = 11.1%. ○The data shows a healthy completion rate with over half of all orders successfully delivered, and a relatively low return rate at 5.2%. ✓ Partially Supported⚓ Partially Grounded The quantitative parts (delivered > 50%, return rate 5.2%) are supported by tool output; the evaluative wording ('healthy', 'successfully') is interpretive and not strictly verifiable from the provided data. 🧑⚖️ Judge ReasoningAll central claims (counts per status and which status is most common) exactly match both the Ground Truth and the tool output. Derived totals and percentages are correctly computed and grounded in the returned counts. Only minor interpretive language about 'healthy' completion is not strictly verifiable, though the underlying metrics cited are accurate.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.9 Groundedness 9.9 Relevance 3.3 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 3.3 Cost 7.0 Err Rate 10.0 Overall 8.8 💬 Respuesta del agente (extracto)## Order Status Summary for 'benchmark-ecommerce' Index
### Orders Per Status
| Status | Order Count |
|--------|-------------|
| delivered | 522 |
| shipped | 123 |
| cancelled | 111 |
| processing | 97 |
| pending | 95 |
| returned | 52 |
| **Total** | **1,000** |
### Key Findings
- **Most common status:** `delivered` with 522 orders (52.2% of all orders)
- **Least common status:** `returned` with 52 orders (5.2% of all orders)
- **Completed orders** (delivered + shipped + returned): 697 orders (69.7%)
- **In-progress orders** (pending + processing): 192 orders (19.2%)
- **Cancelled orde
| ||||||||||||
| ✅ search_01 🔗 | 💬 easy | tool_usage | List all indices in my Elasticsearch cluster | 34.4s | $0.0293 | 9.2 | 7.7 | 10.0 | 7.0 | 8.10 | 7 2? | |
❓ PreguntaList all indices in my Elasticsearch cluster
🎯 GROUND TRUTH:
The agent should use the list_indices tool and return a list of indices in the cluster. The list must include 'benchmark-ecommerce' (1000 order documents) and 'benchmark-customers' (20 customer documents). The cluster contains approximately 47+ indices in total. 📋 Claim Decomposition📋 Claim Analysis
7 claims (3 central) · 2 ungrounded ●The cluster has 47 standalone indices. ✓ Partially Supported⚓ Grounded Tool output explicitly lists 47 index objects under "indices" (matching the agent’s count), but Ground Truth only states the cluster contains approximately 47+ indices (not an exact 47), so the exactness is only partially supported vs Ground Truth. ●The cluster includes indices named "benchmark-customers" and "benchmark-ecommerce". ✓ Fully Supported⚓ Grounded Both names are required by Ground Truth and are present in the tool output’s indices list. ●The full list of standalone indices is: backup, benchmark-customers, benchmark-ecommerce, borrado, borrado_pipeline, busqueda_semantica, byviz_test_indexacion, content-notion-dd4f, dataset, dataset_vectores, elastic_canvas, estados, estados_1, indice_1, indice_2, indice_completo, kb_notion, llm-benchmark-results, llm-benchmark-summary, logs-cloud_security_posture.scores-default, logs-cloud_security_posture.vulnerabilities_latest-default, logs-tenant, logs-ti_abusech_latest.dest_ja3_fingerprints-3, logs-ti_abusech_latest.dest_malware-3, logs-ti_abusech_latest.dest_malwarebazaar-3, logs-ti_abusech_latest.dest_sslblacklist-3, logs-ti_abusech_latest.dest_threatfox-3, logs-ti_abusech_latest.dest_url-3, metrics-endpoint.metadata_current_default, ml_beaconing-1.3.0, my-index-inference, my-index-inference-e5, partial-restored-.ds-indice_frozen-2026.02.12-000002, pedido_clientes, pedidos, pipeline1, pipeline1-borrado, pipeline1-reindex, prueba2-000005, prueba_pdf_1, risk-score.risk-score-latest-default, security_solution-aws.misconfiguration_latest-v2, security_solution-awsconfig.misconfiguration_latest-v1, security_solution-awsinspector.vulnerability_latest-v1, security_solution-cloud_security_posture.misconfiguration_latest-v1, slowlog_iam. ✓ Fully Supported⚓ Grounded This list matches the "indices" names shown in the tool output. ○There are 23 aliases in the cluster. ✓ Not Verifiable⚓ Partially Grounded The tool output includes an "aliases" section but it is truncated, so the exact alias count (23) cannot be confirmed from the provided raw tool output; Ground Truth does not specify alias counts. ○Aliases include "entities-host-history", "entities-host-latest", "entities-host-reset", "entities-service-history", "entities-service-latest", "entities-service-reset", "entities-user-history", "entities-user-latest", and "entities-user-reset". ✓ Not Verifiable⚓ Partially Grounded At least "entities-host-history" is visible in the (truncated) tool output alias list; the remainder may be present but cannot be confirmed from the truncated snippet, and Ground Truth is silent on aliases. ○The cluster has 67 data streams, with examples such as bbva, elastic-cloud-logs-9, kibana_sample_data_logs, and various logs-* / metrics-* / traces-* streams. ✓ Not Verifiable⚓ Ungrounded [major] The provided tool output shows indices and aliases (aliases truncated) but does not show any data streams section or counts; Ground Truth does not mention data streams. Presenting an exact data stream count and many specific names without tool support is a significant ungrounded addition. ○Total: 47 standalone indices + 23 aliases + 67 data streams. ✓ Not Verifiable⚓ Ungrounded [major] Only the 47 indices are directly supported by tool output; the alias count is not confirmable due to truncation and the data stream count is not shown at all. 🧑⚖️ Judge ReasoningThe agent correctly used the tool output to list the standalone indices (including the required benchmark indices) and matched the visible index list. However, it added extensive claims about aliases and especially data streams (including exact counts and many names) that are not supported by the provided tool output and are not required by the user’s request. Formatting is clear and structured, but instruction focus is diluted by ungrounded extra sections.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.list_indices
USED: platform.core.list_indices
📊 Todas las puntuacionesCorrectness 9.2 Groundedness 7.7 Relevance 4.3 Format 9.0 Tool Calling 10.0 Instruct. 7.0 Latency 5.1 Cost 6.5 Err Rate 10.0 Overall 8.1 💬 Respuesta del agente (extracto)Here are all the indices in your Elasticsearch cluster:
## Standalone Indices (47)
| Index Name |
|------------|
| backup |
| benchmark-customers |
| benchmark-ecommerce |
| borrado |
| borrado_pipeline |
| busqueda_semantica |
| byviz_test_indexacion |
| content-notion-dd4f |
| dataset |
| dataset_vectores |
| elastic_canvas |
| estados |
| estados_1 |
| indice_1 |
| indice_2 |
| indice_completo |
| kb_notion |
| llm-benchmark-results |
| llm-benchmark-summary |
| logs-cloud_security_posture.scores-default |
| logs-cloud_security_posture.vulnerabilities_latest-default |
| logs-tenant |
| lo
| ||||||||||||
| ✅ search_02 🔗 | 💬 medium | tool_usage | Search for orders from customers in Madrid in… | 79.5s | $0.0347 | 0.0 | 7.7 | 10.0 | 6.0 | 5.45 | 9 1✗ 2? | |
❓ PreguntaSearch for orders from customers in Madrid in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should search benchmark-ecommerce filtering by customer.city=Madrid. There are exactly 49 orders, all from a single customer: María García López. The agent should return matching documents or a summary of them. 📋 Claim Decomposition📋 Claim Analysis
9 claims (6 central) · 1 contradicted · 2 ungrounded ●Found 50 orders from customers in Madrid in the `benchmark-ecommerce` index. ✓ Contradicted⚓ Ungrounded [critical] Ground truth says there are exactly 49 orders (not 50). The tool output shown is a truncated table without an explicit total count, so the stated count of 50 is not supported by the provided tool output. ●All Madrid orders are from a single customer. ✓ Fully Supported⚓ Partially Grounded Ground truth states all 49 orders are from a single customer. The visible tool rows show the same customer name/email for Madrid, but because the output is truncated we cannot confirm every row from the tool output segment shown. ●The single customer is named María García López. ✓ Fully Supported⚓ Grounded Ground truth explicitly names María García López, and the tool output rows shown include customer.name = "María García López". ●The customer's email is maria.garcia@email.com. ✓ Fully Supported⚓ Grounded The tool output rows shown list customer.email = "maria.garcia@email.com", consistent with the agent response. ●The customer location is Madrid, Spain. ✓ Fully Supported⚓ Grounded The tool output rows shown include customer.city = "Madrid" and customer.country = "Spain". ○Order Status breakdown: delivered (20), shipped (13), cancelled (5), processing (6), pending (2), returned (2). ✓ Not Verifiable⚓ Ungrounded [major] Ground truth does not provide any status distribution. The tool output excerpt is truncated and does not show aggregated counts by status, so these specific numbers are not supported by the provided tool output. ○Sample order ORD-2025-00011 (Set Herramientas 50 piezas) is delivered with total amount €99.48 and order date 2025-07-09. ✓ Not Verifiable⚓ Grounded Ground truth does not list individual order details. The tool output explicitly shows order_id ORD-2025-00011, product name, status delivered, total_amount ~99.48, and order_date 2025-07-09. ○Sample order ORD-2025-00037 (Cafetera Automática Deluxe) is cancelled with total amount €1,059.61 and order date 2025-12-27. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not list individual order details. The tool output shows order_id ORD-2025-00037 and order_date 2025-12-27, but the displayed excerpt is truncated before confirming the product, status, and total amount. ●The query used was: FROM benchmark-ecommerce | WHERE customer.city == "Madrid" | LIMIT 100. ✓ Fully Supported⚓ Grounded The tool output includes the exact ES|QL query shown by the agent. 🧑⚖️ Judge ReasoningThe response correctly identifies the Madrid filter and the single-customer attribution (María García López) and accurately reproduces the ES|QL query. However, it fails the exact-answer requirement by reporting 50 orders instead of the correct 49, and it introduces an unsubstantiated status-count breakdown not supported by the provided tool output excerpt.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ❌EXPECTED: 49FOUND: 50📊 Todas las puntuacionesCorrectness 0.0 Groundedness 7.7 Relevance 6.7 Format 9.0 Tool Calling 10.0 Instruct. 6.0 Latency 1.7 Cost 6.3 Err Rate 10.0 Overall 5.5 💬 Respuesta del agente (extracto)Found **50 orders** from customers in Madrid in the `benchmark-ecommerce` index. All orders are from a single customer:
**Customer Details:**
- **Name:** María García López
- **Email:** maria.garcia@email.com
- **Location:** Madrid, Spain
**Order Summary:**
| Metric | Details |
|--------|---------|
| **Order Status** | delivered (20), shipped (13), cancelled (5), processing (6), pending (2), returned (2) |
| **Payment Methods** | credit_card, debit_card, paypal, bank_transfer, cash_on_delivery |
| **Product Categories** | electronics, home, clothing, books, bags, accessories |
| **Order Dat
| ||||||||||||
| ✅ search_03 🔗 | 💬 easy | tool_usage | Show me the mapping of the 'benchmark-ecommer… | 21.2s | $0.0083 | 7.5 | 9.9 | 10.0 | 9.0 | 8.71 | 7 1✗ | |
❓ PreguntaShow me the mapping of the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should use the get_index_mapping tool and display the field types and structure of the benchmark-ecommerce index. Key fields include: order_id (keyword), customer (object with name/text, city/keyword, country/keyword, email/keyword), product (object with name/text, category/keyword, subcategory/keyword), status (keyword), total_amount (float), order_date (date), payment_method (keyword), shipping_method (keyword), priority (keyword), quantity (integer), discount_pct (float), rating (float), tags (keyword), notes (text). 📋 Claim Decomposition📋 Claim Analysis
7 claims (5 central) · 1 contradicted · all grounded ●The `benchmark-ecommerce` index mapping includes a `customer` object with subfields `city` (keyword), `country` (keyword), `email` (keyword), and `name` (text) with a `keyword` multi-field. ✓ Fully Supported⚓ Grounded Ground truth lists these customer fields and types; tool output shows the same structure including `name` as `text` with `fields.keyword`. ●The mapping includes a `product` object with `category` (keyword), `subcategory` (keyword), and `name` (text) with a `keyword` multi-field. ✓ Fully Supported⚓ Grounded Ground truth expects these product fields/types, and the tool output exactly matches the provided mapping. ●The mapping includes order-related fields: `order_id` (keyword), `order_date` (date), `status` (keyword), `priority` (keyword), `payment_method` (keyword), `quantity` (integer), `discount_pct` (float), `total_amount` (float), `tags` (keyword), and `notes` (text). ✓ Fully Supported⚓ Grounded All listed fields and types align with the ground truth’s key fields and are directly present in the tool output mapping. ●The mapping defines `rating` as type `integer`. ✓ Contradicted⚓ Grounded [major] Tool output clearly shows `rating` is `integer` (so grounded), but the provided ground truth expects `rating` to be `float`, so it contradicts the expected answer. ○The mapping includes additional fields: `delivered_at` (date), `estimated_delivery` (date), `is_gift` (boolean), `shipping_cost` (float), `shipping_method` (keyword), `tax_amount` (float), and `unit_price` (float). ✓ Not Verifiable⚓ Grounded These fields are not mentioned in the ground truth list (so correctness vs that reference is not directly verifiable), but they are explicitly present in the tool output mapping. ●The response’s JSON snippet represents the mapping for the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded The JSON shown matches the tool output’s `properties` content for the index mapping. ○Type descriptions given (e.g., `keyword` for exact matches, `text` for full-text search, `date` for timestamps, `float` for decimal numbers, `integer` for whole numbers, `boolean` for true/false flags) correctly describe these Elasticsearch field types. ✓ Not Verifiable⚓ Disclosed Ungrounded These are general explanations not derived from the tool output; the ground truth does not evaluate these definitions. 🧑⚖️ Judge ReasoningThe agent correctly used and reproduced the tool-derived mapping structure and most key fields/types, and the output is well formatted with a clear JSON block and summaries. The main issue relative to the provided ground truth is the `rating` type: the agent reports `integer` (matching the tool output) while the expected answer states `float`.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 7.5 Groundedness 9.9 Relevance 7.1 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 6.4 Cost 9.3 Err Rate 10.0 Overall 8.7 💬 Respuesta del agente (extracto)Here is the mapping for the **`benchmark-ecommerce`** index:
```json
{
"mappings": {
"properties": {
"customer": {
"properties": {
"city": { "type": "keyword" },
"country": { "type": "keyword" },
"email": { "type": "keyword" },
"name": {
"type": "text",
"fields": { "keyword": { "type": "keyword" } }
}
}
},
"delivered_at": { "type": "date" },
"discount_pct": { "type": "float" },
"estimated_delivery": { "type": "date" },
"is_gift": { "type": "boolean" },
"no
| ||||||||||||
| ✅ search_04 🔗 | 💬 hard | tool_usage | Find all cancelled orders in the 'benchmark-e… | 78.0s | $0.0326 | 9.6 | 8.0 | 10.0 | 9.0 | 8.03 | 8 2? | |
❓ PreguntaFind all cancelled orders in the 'benchmark-ecommerce' index that have a total_amount greater than 500
🎯 GROUND TRUTH:
The agent should search for documents where status=cancelled and total_amount>500 in the benchmark-ecommerce index. There are exactly 40 such orders. 📋 Claim Decomposition📋 Claim Analysis
8 claims (2 central) · 2 ungrounded ●There are 40 cancelled orders in the `benchmark-ecommerce` index with `total_amount` greater than 500. ✓ Fully Supported⚓ Grounded Matches the Ground Truth exact expected answer (40). Tool output shows an ES|QL query for the same criteria and returns tabular results (truncated), consistent with the agent summarizing a 40-row result set. ●The applied criteria were `status: cancelled` AND `total_amount > 500` in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded This exactly matches the Ground Truth filtering requirement and is explicitly present in the tool output ES|QL: `WHERE status == "cancelled" AND total_amount > 500`. ○Example matching order: ORD-2025-00014 (John Smith, United Kingdom) has status cancelled and total_amount 2,841.48 with order date 2025-10-25. ✓ Not Verifiable⚓ Grounded Ground Truth only specifies the final count, not specific orders. The tool output explicitly includes a row for ORD-2025-00014 with status "cancelled", total_amount 2841.47998046875, and order_date 2025-10-25. ○Example matching order: ORD-2025-00020 (Pedro Sánchez Díaz, Spain) has status cancelled and total_amount 899.71 with order date 2025-07-11. ✓ Not Verifiable⚓ Grounded Not checkable against Ground Truth (which doesn’t list specific orders), but the tool output shows ORD-2025-00020 with status "cancelled" and an order_date of 2025-07-11; the row is present in the returned tabular data (truncated). ○The total_amount range for these cancelled orders is approximately 524 to 7,351. ✓ Not Verifiable⚓ Partially Grounded Ground Truth provides only the count (40), not min/max. The tool output is truncated and does not explicitly show computed min/max range; while values shown include amounts far above 500, the stated full range is not directly evidenced. ○The matching orders come from countries including Spain, United Kingdom, Germany, Italy, and France. ✓ Not Verifiable⚓ Partially Grounded Ground Truth doesn’t specify country distribution. Tool output rows shown include United Kingdom and Spain, and the truncated dataset may include others, but the full list stated is not directly confirmed in the visible tool output. ○The top 5 highest value matching orders include ORD-2025-00761 (7,350.64) and ORD-2025-00423 (6,087.85). ✓ Not Verifiable⚓ Ungrounded [major] Ground Truth doesn’t contain these order IDs/amounts. The tool output shows no explicit sorting/aggregation or 'top 5' computation, and the visible truncated rows do not include these specific orders, so this appears to be an unsupported inference/hallucination. ○Payment method distribution: Credit Card is most common; PayPal frequent; Debit Card common; Cash on Delivery some; Bank Transfer few. ✓ Not Verifiable⚓ Ungrounded [major] Ground Truth provides no payment-method breakdown. Tool output is a simple filtered result set (no aggregation) and does not present any distribution analysis; the claim asserts a computed frequency ranking without supporting tool evidence. 🧑⚖️ Judge ReasoningThe agent’s core answer (40) and the filter criteria match the Ground Truth exactly, and the tool output confirms the intended ES|QL filter. However, several added summaries (range, country list) are not directly verifiable from the provided Ground Truth and are only weakly supported by the truncated output. Claims about 'top 5 highest value orders' and payment-method distribution are not supported by the tool output and require aggregations/sorting not shown.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 40FOUND: 40📊 Todas las puntuacionesCorrectness 9.6 Groundedness 8.0 Relevance 2.5 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 1.8 Cost 6.4 Err Rate 10.0 Overall 8.0 💬 Respuesta del agente (extracto)## Cancelled Orders with Total Amount > 500
Found **40 cancelled orders** in the `benchmark-ecommerce` index with `total_amount` greater than 500.
### Summary Statistics
| Metric | Value |
|--------|-------|
| Total Orders | 40 |
| Amount Range | ~524 - 7,351 |
| Countries | Spain, United Kingdom, Germany, Italy, France |
### Sample Results
| Order ID | Customer | Country | Product | Total Amount | Order Date |
|----------|----------|---------|---------|--------------|------------|
| ORD-2025-00014 | John Smith | United Kingdom | Tablet Air 10 | 2,841.48 | 2025-10-25 |
| ORD-2025-00020 | P
| ||||||||||||
| Test | Type | Category | Question | Latency | Cost | Corr | Grnd | Tool | Instr | Overall | Claims | Tools |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ✅ adversarial_01 🔗 | 💬 hard | adversarial | Show me the distribution of the 'discount_typ… | 78.5s | $0.0865 | 10.0 | 10.0 | 10.0 | 10.0 | 8.86 | 9 | |
❓ PreguntaShow me the distribution of the 'discount_type' field in the 'benchmark-ecommerce' index. Group by discount_type and show counts.
🎯 GROUND TRUTH:
The field 'discount_type' does NOT exist in the index (the actual field is 'discount_pct'). The agent should either report that the field doesn't exist, suggest the correct field, or handle the error gracefully. Tests handling of non-existent fields. 📋 Claim Decomposition📋 Claim Analysis
9 claims (9 central) · all grounded ●The `discount_type` field does not exist in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Ground truth states `discount_type` does not exist, and the index mapping tool output shows no `discount_type` property. ●There is a `discount_pct` (discount percentage) field in the index. ✓ Fully Supported⚓ Grounded The mapping tool output includes `discount_pct` (type float), confirming the field exists. ●The response provides the distribution of records grouped by discount percentage (`discount_pct`). ✓ Fully Supported⚓ Grounded Tool output shows an ESQL query grouping by `discount_pct` with counts; the agent presents this as the distribution. ●There are 376 records with discount percentage 0. ✓ Fully Supported⚓ Grounded Tool tabular_data includes the row [376, 0] for (count, discount_pct). ●There are 144 records with discount percentage 5. ✓ Fully Supported⚓ Grounded Tool tabular_data includes the row [144, 5] for (count, discount_pct). ●There are 118 records with discount percentage 10. ✓ Fully Supported⚓ Grounded Tool tabular_data includes the row [118, 10] for (count, discount_pct). ●There are 132 records with discount percentage 15. ✓ Fully Supported⚓ Grounded Tool tabular_data includes the row [132, 15] for (count, discount_pct). ●There are 114 records with discount percentage 20. ✓ Fully Supported⚓ Grounded Tool tabular_data includes the row [114, 20] for (count, discount_pct). ●There are 116 records with discount percentage 25. ✓ Fully Supported⚓ Grounded Tool tabular_data includes the row [116, 25] for (count, discount_pct). 🧑⚖️ Judge ReasoningThe agent correctly identified that `discount_type` does not exist and appropriately redirected to the correct field `discount_pct`, aligning with the ground truth. All reported counts match the tool output exactly, and the response is clearly formatted and follows the user’s intent while handling the missing-field case gracefully.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 1.8 Cost 3.8 Err Rate 10.0 Overall 8.9 💬 Respuesta del agente (extracto)The `discount_type` field does not exist in the `benchmark-ecommerce` index. However, there is a `discount_pct` (discount percentage) field.
Here is the distribution of records based on the discount percentage:
| Discount Percentage | Count |
| :--- | :--- |
| 0 | 376 |
| 5 | 144 |
| 10 | 118 |
| 15 | 132 |
| 20 | 114 |
| 25 | 116 |
| ||||||||||||
| ❌ adversarial_02 🔗 | 💬 hard | adversarial | Find all orders in 'benchmark-ecommerce' wher… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaFind all orders in 'benchmark-ecommerce' where the status is both 'cancelled' AND 'delivered' at the same time
🎯 GROUND TRUTH:
This is a contradictory request — an order cannot be both cancelled and delivered. The result should be 0 orders. The agent should recognize and communicate this contradiction or return empty results gracefully. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ❌ adversarial_03 🔗 | 💬 expert | adversarial | Perform a SQL JOIN between the 'benchmark-eco… | 120.2s | — | ⏱ Timeout | ||||||
❓ PreguntaPerform a SQL JOIN between the 'benchmark-ecommerce' and 'benchmark-customers' indices on the customer email field and return the top 5 results
🎯 GROUND TRUTH:
Elasticsearch does not support SQL JOINs natively. The agent should explain this limitation and potentially suggest an alternative approach (e.g., querying both indices separately, or using ES|QL LOOKUP). Tests handling of impossible operations. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ❌ cross_01 🔗 | 💬 expert | cross_index | First, find the 'Gold' tier customers in 'ben… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaFirst, find the 'Gold' tier customers in 'benchmark-customers'. Then, search for their orders in 'benchmark-ecommerce' by matching customer name. How many orders do Gold tier customers have in total?
🎯 GROUND TRUTH:
The agent must: 1) Query benchmark-customers WHERE tier='Gold' — there are 2 Gold customers: Laura Martínez Gómez and Sofia Torres Navarro, 2) Search benchmark-ecommerce for their orders: Laura has 54 orders and Sofia has 58 orders, 3) Total Gold tier orders: 112. Tests cross-index reasoning requiring multiple tool calls. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ❌ cross_02 🔗 | 💬 hard | cross_index | Show me the benchmark-customers index mapping… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaShow me the benchmark-customers index mapping. Then tell me: which customer tier (Standard, Premium, Gold, Platinum) has the most customers?
🎯 GROUND TRUTH:
The agent should get the mapping of benchmark-customers, then query to aggregate by tier. The exact distribution is: Standard (11 customers, 55%), Premium (6 customers, 30%), Gold (2 customers, 10%), Platinum (1 customer, 5%). Standard has the most customers. There are 20 customers total. Tests discovery and querying of a new index. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ edge_01 🔗 | 💬 easy | edge_case | Search for documents about 'quantum_headphone… | 19.8s | $0.0233 | 10.0 | 7.0 | 10.0 | 9.0 | 8.79 | 1 | |
❓ PreguntaSearch for documents about 'quantum_headphones_xyz_404' in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should attempt the search and gracefully report that no results were found for this nonexistent product. Tests zero-result handling. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●There are no documents matching 'quantum_headphones_xyz_404' in the 'benchmark-ecommerce' index. ✓ Fully Supported⚓ Partially Grounded Correctness: Matches the ground truth expectation to report zero results for a nonexistent product. Groundedness: The tool output shown is empty (no results displayed), which is consistent with zero hits, but it does not explicitly state 'no documents found,' so the claim is only implicitly supported. 🧑⚖️ Judge ReasoningThe agent directly answered the query with the expected zero-results outcome, aligning with the ground truth. However, the provided tool output contains no explicit hit/total information, so the response is only indirectly supported by the tool trace. Formatting is clear and compliant with the user’s request.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 7.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 9.0 Latency 6.5 Cost 6.8 Err Rate 10.0 Overall 8.8 💬 Respuesta del agente (extracto)There are no documents matching 'quantum_headphones_xyz_404' in the 'benchmark-ecommerce' index.
| ||||||||||||
| ✅ edge_02 🔗 | 💬 medium | edge_case | show me everything about the ecommerce data | 66.2s | $0.1279 | 9.8 | 9.6 | 10.0 | 9.0 | 8.64 | 8 | |
❓ Preguntashow me everything about the ecommerce data
🎯 GROUND TRUTH:
The agent should handle this vague request by making a reasonable interpretation — perhaps showing a sample of documents, the mapping, or a summary of the benchmark-ecommerce index. 📋 Claim Decomposition📋 Claim Analysis
8 claims (8 central) · all grounded ●There is an index named `benchmark-ecommerce` configured for ecommerce data. ✓ Fully Supported⚓ Grounded Tool output from list_indices shows an index named `benchmark-ecommerce`, matching the agent’s statement and aligning with the expected interpretation of the request. ●The `benchmark-ecommerce` index is currently empty and contains no documents. ✓ Not Verifiable⚓ Partially Grounded Ground truth does not specify whether the index has documents. The search output shown is blank (no hits displayed), which suggests emptiness but does not explicitly confirm it. ●The index’s structure (mapping) is fully defined. ✓ Fully Supported⚓ Grounded Tool output includes a full mapping for `benchmark-ecommerce`, supporting that the structure is defined; this also matches the ground-truth expectation to show mapping/summary. ●The mapping includes Order Details fields: `order_id` (keyword), `order_date` (date), `status` (keyword), `priority` (keyword), `is_gift` (boolean), `notes` (text), and `tags` (keyword). ✓ Fully Supported⚓ Grounded Each listed field and type appears in the mapping tool output under `properties` with the specified types. ●The mapping includes Customer Information fields: `customer.name` (text with keyword subfield), `customer.email` (keyword), `customer.city` (keyword), and `customer.country` (keyword). ✓ Fully Supported⚓ Grounded The mapping shows `customer` properties with the stated types, including `name` as text with a `keyword` multi-field. ●The mapping includes Product Information fields: `product.name` (text with keyword subfield), `product.category` (keyword), `product.subcategory` (keyword), `quantity` (integer), and `rating` (integer). ✓ Fully Supported⚓ Grounded All these fields and types are present in the mapping output (product sub-properties plus top-level `quantity` and `rating`). ●The mapping includes Financial fields: `unit_price` (float), `discount_pct` (float), `tax_amount` (float), `shipping_cost` (float), and `total_amount` (float). ✓ Fully Supported⚓ Grounded The mapping tool output lists each of these fields with type `float`. ●The mapping includes Shipping & Delivery fields: `shipping_method` (keyword), `estimated_delivery` (date), and `delivered_at` (date), and includes `payment_method` (keyword). ✓ Fully Supported⚓ Grounded All mentioned fields and types appear in the mapping output exactly as described. 🧑⚖️ Judge ReasoningThe response is largely a faithful summary of the available tool output: it correctly identifies the `benchmark-ecommerce` index and accurately lists the mapping fields and types. The main weak point is the assertion that the index is empty, which is not explicitly shown in the provided search output and is not required by the ground truth, though it is suggested by the blank search result display.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search platform.core.list_indices platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 9.8 Groundedness 9.6 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 2.6 Cost 2.2 Err Rate 10.0 Overall 8.6 💬 Respuesta del agente (extracto)There is an index named `benchmark-ecommerce` configured for ecommerce data. While the index is currently empty and contains no documents, its structure is fully defined.
Here is the complete schema and the fields available for the ecommerce data:
**Order Details**
* `order_id` (keyword)
* `order_date` (date)
* `status` (keyword)
* `priority` (keyword)
* `is_gift` (boolean)
* `notes` (text)
* `tags` (keyword)
**Customer Information**
* `customer.name` (text / keyword)
* `customer.email` (keyword)
* `customer.city` (keyword)
* `customer.country` (keyword)
**Product In
| ||||||||||||
| ✅ edge_03 🔗 | 💬 easy | edge_case | Get the mapping for an index called 'this_ind… | 17.4s | $0.0195 | 10.0 | 10.0 | 10.0 | 9.0 | 9.13 | 2 | |
❓ PreguntaGet the mapping for an index called 'this_index_definitely_does_not_exist_12345'
🎯 GROUND TRUTH:
The agent should attempt to get the mapping and gracefully handle the error when the index doesn't exist. 📋 Claim Decomposition📋 Claim Analysis
2 claims (2 central) · all grounded ●The index `this_index_definitely_does_not_exist_12345` does not exist. ✓ Fully Supported⚓ Grounded The tool output shows an `index_not_found_exception` for this exact index name, supporting that it does not exist; this aligns with the expected behavior of handling a missing index. ●Because the index does not exist, its mapping cannot be retrieved. ✓ Fully Supported⚓ Grounded The mapping request failed with `index_not_found_exception`, which implies the mapping cannot be fetched for a non-existent index; this matches the ground truth expectation to attempt retrieval and handle the error. 🧑⚖️ Judge ReasoningThe agent’s response accurately summarizes the tool error indicating the index is missing and correctly concludes that the mapping cannot be retrieved. It is grounded in the tool output and aligns with the expected graceful handling, though it does not explicitly mention that it attempted the retrieval (even though the tool call shows it did).
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 9.0 Latency 6.8 Cost 7.1 Err Rate 7.0 Overall 9.1 💬 Respuesta del agente (extracto)The index `this_index_definitely_does_not_exist_12345` does not exist, so its mapping cannot be retrieved.
| ||||||||||||
| ✅ esql_01 🔗 | 💬 easy | analytics | Using ES|QL, count how many orders are in the… | 32.4s | $0.0635 | 10.0 | 10.0 | 10.0 | 10.0 | 9.27 | 3 | |
❓ PreguntaUsing ES|QL, count how many orders are in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should query the benchmark-ecommerce index and return 1000 as the document count. The tool may use ES|QL internally via platform.core.search. 📋 Claim Decomposition📋 Claim Analysis
3 claims (3 central) · all grounded ●There are 1,000 orders in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Matches the ground truth expected answer (1000) and is directly supported by the tool output showing total_orders = 1000. ●You can retrieve this count using the ES|QL query `FROM benchmark-ecommerce | STATS total_orders = COUNT(*)`. ✓ Fully Supported⚓ Grounded The tool output includes the same ES|QL query used to compute the count, and it correctly returns the count. ●The provided ES|QL query counts the total number of orders/documents in the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded COUNT(*) over `FROM benchmark-ecommerce` is a document count; the tool output confirms it produces total_orders = 1000. 🧑⚖️ Judge ReasoningThe agent’s response contains the correct exact count (1000) and provides the same ES|QL query shown in the tool output. All factual claims are directly grounded in the tool results and fully supported by the ground truth. Formatting is clear and instructions are followed.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 1000FOUND: 1000📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.3 Cost 4.8 Err Rate 10.0 Overall 9.3 💬 Respuesta del agente (extracto)There are 1,000 orders in the `benchmark-ecommerce` index.
You can retrieve this count using the following ES|QL query:
```esql
FROM benchmark-ecommerce
| STATS total_orders = COUNT(*)
```
| ||||||||||||
| ✅ esql_02 🔗 | 💬 hard | analytics | Using ES|QL on the 'benchmark-ecommerce' inde… | 62.1s | $0.1597 | 10.0 | 10.0 | 10.0 | 10.0 | 8.54 | 9 | |
❓ PreguntaUsing ES|QL on the 'benchmark-ecommerce' index, show me the number of orders and total revenue per product category, sorted by revenue descending
🎯 GROUND TRUTH:
The agent should group by product.category, calculate COUNT(*) and SUM(total_amount), then sort by total revenue descending. The exact results are: electronics (413 orders, €686,037.84), home (203 orders, €137,010.55), clothing (186 orders, €45,103.57), sports (42 orders, €20,303.18), bags (52 orders, €16,039.01), books (104 orders, €15,702.88). Electronics is the top category by a wide margin. 📋 Claim Decomposition📋 Claim Analysis
9 claims (9 central) · all grounded ●The ES|QL query used was: FROM benchmark-ecommerce | STATS orders = COUNT(order_id), total_revenue = SUM(total_amount) BY product.category | SORT total_revenue DESC. ✓ Fully Supported⚓ Grounded Ground truth expects grouping by product.category with COUNT and SUM(total_amount) and sorting by revenue desc; the tool output shows this exact ES|QL query (with an added LIMIT 100). ●The results are grouped by product.category with an orders count and a total revenue sum per category. ✓ Fully Supported⚓ Grounded Both ground truth and tool output confirm the aggregation is COUNT(...) and SUM(total_amount) BY product.category. ●Electronics has 413 orders and total revenue of 686,037.84. ✓ Fully Supported⚓ Grounded Matches ground truth exactly and matches tool output values (686037.8392..., rounded to 686,037.84). ●Home has 203 orders and total revenue of 137,010.55. ✓ Fully Supported⚓ Grounded Matches ground truth exactly and matches tool output values (137010.5497..., rounded to 137,010.55). ●Clothing has 186 orders and total revenue of 45,103.57. ✓ Fully Supported⚓ Grounded Matches ground truth exactly and matches tool output values (45103.5699..., rounded to 45,103.57). ●Sports has 42 orders and total revenue of 20,303.18. ✓ Fully Supported⚓ Grounded Matches ground truth exactly and matches tool output values (20303.1800..., rounded to 20,303.18). ●Bags has 52 orders and total revenue of 16,039.01. ✓ Fully Supported⚓ Grounded Matches ground truth exactly and matches tool output values (16039.0099..., rounded to 16,039.01). ●Books has 104 orders and total revenue of 15,702.88. ✓ Fully Supported⚓ Grounded Matches ground truth exactly and matches tool output values (15702.8799..., rounded to 15,702.88). ●The results are sorted by total revenue in descending order (highest revenue category first). ✓ Fully Supported⚓ Grounded The query includes SORT total_revenue DESC and the returned ordering in the tool output and agent table is consistent with descending revenue. 🧑⚖️ Judge ReasoningThe agent provided the correct ES|QL query structure (group by product.category, COUNT orders, SUM revenue, sort by revenue descending) and reported category-level totals that match both the ground truth and the tool output (with appropriate rounding). The response is clearly formatted with a code block and a results table and fully satisfies the user request.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 2.9 Cost 1.0 Err Rate 7.0 Overall 8.5 💬 Respuesta del agente (extracto)Here is the ES|QL query used to aggregate the data:
```esql
FROM benchmark-ecommerce
| STATS orders = COUNT(order_id), total_revenue = SUM(total_amount) BY product.category
| SORT total_revenue DESC
```
### Results
| Product Category | Orders | Total Revenue |
| :--- | :--- | :--- |
| electronics | 413 | 686,037.84 |
| home | 203 | 137,010.55 |
| clothing | 186 | 45,103.57 |
| sports | 42 | 20,303.18 |
| bags | 52 | 16,039.01 |
| books | 104 | 15,702.88 |
| ||||||||||||
| ✅ esql_03 🔗 | 💬 hard | analytics | Using ES|QL, show me the average order value … | 35.0s | $0.0740 | 10.0 | 10.0 | 10.0 | 9.0 | 9.12 | 8 | |
❓ PreguntaUsing ES|QL, show me the average order value by payment method in the 'benchmark-ecommerce' index, but only for delivered orders
🎯 GROUND TRUTH:
The agent should filter WHERE status=='delivered' (522 orders), then group by payment_method and calculate AVG(total_amount). Expected averages: credit_card €895.16 (167 orders), paypal €889.08 (136 orders), debit_card €852.93 (110 orders), bank_transfer €764.34 (63 orders), cash_on_delivery €744.24 (46 orders). 📋 Claim Decomposition📋 Claim Analysis
8 claims (8 central) · all grounded ●The provided ES|QL query returns the average order value by payment method for delivered orders in the benchmark-ecommerce index. ✓ Fully Supported⚓ Grounded Ground truth expects filtering to delivered orders and averaging total_amount grouped by payment_method; the tool output shows the same intent and results from that exact query pattern. ●The ES|QL query filters to delivered orders using: WHERE status == "delivered". ✓ Fully Supported⚓ Grounded This matches the ground truth requirement and appears verbatim in the tool output query. ●The ES|QL query computes average order value as AVG(total_amount) grouped by payment_method (STATS avg_order_value = AVG(total_amount) BY payment_method). ✓ Fully Supported⚓ Grounded This is exactly what the ground truth specifies and matches the tool output query and returned columns. ●For delivered orders, the average order value for Credit Card is 895.16. ✓ Fully Supported⚓ Grounded Ground truth lists credit_card €895.16; tool output shows 895.164011..., which rounds to 895.16. ●For delivered orders, the average order value for PayPal is 889.08. ✓ Fully Supported⚓ Grounded Ground truth lists paypal €889.08; tool output shows 889.077351..., which rounds to 889.08. ●For delivered orders, the average order value for Debit Card is 852.93. ✓ Fully Supported⚓ Grounded Ground truth lists debit_card €852.93; tool output shows 852.928359..., which rounds to 852.93. ●For delivered orders, the average order value for Bank Transfer is 764.34. ✓ Fully Supported⚓ Grounded Ground truth lists bank_transfer €764.34; tool output shows 764.337944..., which rounds to 764.34. ●For delivered orders, the average order value for Cash on Delivery is 744.24. ✓ Fully Supported⚓ Grounded Ground truth lists cash_on_delivery €744.24; tool output shows 744.237170..., which rounds to 744.24. 🧑⚖️ Judge ReasoningThe agent’s ES|QL query correctly filters to delivered orders and computes AVG(total_amount) grouped by payment_method, matching both the ground truth and the tool output. The reported averages align with the tool values (rounded to two decimals) and with the expected results. Minor omission: the agent did not include order counts or explicit currency formatting, but those were not strictly required by the user query.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.get_index_mapping
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 5.0 Cost 4.3 Err Rate 10.0 Overall 9.1 💬 Respuesta del agente (extracto)Here is the ES|QL query to get the average order value by payment method for delivered orders:
```esql
FROM benchmark-ecommerce
| WHERE status == "delivered"
| STATS avg_order_value = AVG(total_amount) BY payment_method
```
**Results:**
* **Credit Card:** 895.16
* **PayPal:** 889.08
* **Debit Card:** 852.93
* **Bank Transfer:** 764.34
* **Cash on Delivery:** 744.24
| ||||||||||||
| ❌ exact_01 🔗 | 💬 medium | exact_answer | How many orders in the 'benchmark-ecommerce' … | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaHow many orders in the 'benchmark-ecommerce' index have status 'shipped' AND total_amount greater than 1000? Give me only the count.
🎯 GROUND TRUTH:
The exact answer is 44. The agent should filter by status=shipped AND total_amount>1000 and return the count. This tests precise numeric accuracy. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
🎯 Exact Answer Check ❌EXPECTED: 44FOUND: None📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ exact_02 🔗 | 💬 medium | exact_answer | What is the total sum of 'total_amount' for a… | 35.3s | $0.0694 | 9.7 | 9.7 | 10.0 | 10.0 | 8.90 | 3 | |
❓ PreguntaWhat is the total sum of 'total_amount' for all orders with status 'delivered' in the 'benchmark-ecommerce' index?
🎯 GROUND TRUTH:
The exact answer is approximately €446,617.23. The agent should aggregate SUM(total_amount) WHERE status='delivered'. Tests precision in numeric aggregation. 📋 Claim Decomposition📋 Claim Analysis
3 claims (2 central) · all grounded ●The total sum of `total_amount` for all orders with the status 'delivered' is 446,617.23. ✓ Fully Supported⚓ Grounded Matches the expected answer exactly (446617.23) and is consistent with the tool output value 446617.22955703735 when rounded to 2 decimals. ●This sum is computed for orders whose status is 'delivered'. ✓ Fully Supported⚓ Grounded Ground truth specifies filtering WHERE status='delivered', and the tool ES|QL explicitly includes WHERE status == "delivered". ○The exact value is 446,617.229557037. ✓ Partially Supported⚓ Partially Grounded Tool output shows 446617.22955703735; the agent’s 'exact value' truncates/rounds off the trailing digits (missing '35'), so it is not exactly identical though it is extremely close. 🧑⚖️ Judge ReasoningThe agent correctly answered the user’s query with the expected total (446617.23) and aligned with the tool’s aggregation and status filter. The only minor issue is labeling a truncated number as an 'exact value' that does not match the tool output to full displayed precision.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 446617.23FOUND: 446617.23📊 Todas las puntuacionesCorrectness 9.7 Groundedness 9.7 Relevance 6.7 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.0 Cost 4.5 Err Rate 10.0 Overall 8.9 💬 Respuesta del agente (extracto)The total sum of `total_amount` for all orders with the status 'delivered' is **446,617.23** (exact value: 446,617.229557037).
| ||||||||||||
| ❌ exact_03 🔗 | 💬 easy | exact_answer | How many unique customers (distinct customer … | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaHow many unique customers (distinct customer names) exist in the 'benchmark-ecommerce' index?
🎯 GROUND TRUTH:
The exact answer is 20. The agent should count distinct customer.name.keyword values. Tests cardinality aggregation. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
🎯 Exact Answer Check ❌EXPECTED: 20FOUND: None📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ expert_01 🔗 | 💬 expert | expert | In the 'benchmark-ecommerce' index, find the … | 92.9s | $0.0915 | 7.7 | 7.8 | 10.0 | 9.0 | 7.34 | 4 | |
❓ PreguntaIn the 'benchmark-ecommerce' index, find the top 3 customers by total revenue from non-cancelled orders. For each customer, show their name, total revenue, number of orders, and average order value. Format as a table.
🎯 GROUND TRUTH:
The agent should filter out cancelled orders (889 non-cancelled orders), group by customer name, calculate SUM(total_amount), COUNT(*), and AVG(total_amount), sort by revenue descending, limit to 3. The exact top 3 are: 1) Hans Mueller (55 orders, €68,600.24 total, avg €1,247.28), 2) Elena Moreno Jiménez (47 orders, €55,882.65 total, avg €1,188.99), 3) Lucía Romero Gutiérrez (43 orders, €52,189.00 total, avg €1,213.70). Tests complex aggregation with derived metrics. 📋 Claim Decomposition📋 Claim Analysis
4 claims (4 central) · all grounded ●The table shows the top 3 customers by total revenue from non-cancelled orders in the 'benchmark-ecommerce' index. ✓ Fully Supported⚓ Grounded Ground truth expects top-3 by SUM(total_amount) with cancelled orders filtered out, and the tool output shows an ESQL query with WHERE status != "cancelled" and LIMIT 3 returning these three customers. ●Hans Mueller has total revenue of $68,600.24 from 55 non-cancelled orders, with an average order value of $1,247.28. ✓ Partially Supported⚓ Partially Grounded The numeric values (68600.24, 55, 1247.28 rounded) match both ground truth and tool output, but the agent labels the currency as dollars ($) while the ground truth expresses amounts in euros (€); tool output provides numbers without currency. ●Elena Moreno Jiménez has total revenue of $55,882.65 from 47 non-cancelled orders, with an average order value of $1,188.99. ✓ Partially Supported⚓ Partially Grounded The numeric values match the ground truth and the tool output (55882.65, 47, 1188.99 rounded), but the response uses a dollar currency symbol not supported by the ground truth (euros) or the tool output (no currency metadata). ●Lucía Romero Gutiérrez has total revenue of $52,189.00 from 43 non-cancelled orders, with an average order value of $1,213.70. ✓ Partially Supported⚓ Partially Grounded The numeric values match ground truth and tool output (52189.00 rounded, 43, 1213.70 rounded), but the response’s use of $ is not grounded and conflicts with the ground truth’s €. 🧑⚖️ Judge ReasoningThe agent correctly identified the top 3 customers and reported the correct totals, order counts, and averages consistent with the tool output and ground truth (allowing for rounding). The main issue is the incorrect/unjustified currency symbol ($ instead of the ground-truth €), which is not supported by the tool output and could mislead interpretation.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 7.7 Groundedness 7.8 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 1.0 Cost 3.6 Err Rate 7.0 Overall 7.3 💬 Respuesta del agente (extracto)Here are the top 3 customers by total revenue for non-cancelled orders:
| Customer Name | Total Revenue | Number of Orders | Average Order Value |
| :--- | :--- | :--- | :--- |
| Hans Mueller | $68,600.24 | 55 | $1,247.28 |
| Elena Moreno Jiménez | $55,882.65 | 47 | $1,188.99 |
| Lucía Romero Gutiérrez | $52,189.00 | 43 | $1,213.70 |
| ||||||||||||
| ✅ expert_02 🔗 | 💬 expert | expert | Compare revenue in Q3 (July-September) vs Q4 … | 65.8s | $0.1161 | 7.7 | 7.8 | 10.0 | 9.0 | 7.76 | 8 | |
❓ PreguntaCompare revenue in Q3 (July-September) vs Q4 (October-December) 2025 by product category in 'benchmark-ecommerce'. Which category grew the most between Q3 and Q4? Which declined the most?
🎯 GROUND TRUTH:
The agent should calculate revenue per category for Q3 (Jul-Sep) and Q4 (Oct-Dec) separately. Exact Q3→Q4 changes: books grew +35.9% (€6,656→€9,047, most growth), electronics declined -18.4% (€377,716→€308,321), clothing declined -10.0% (€23,738→€21,366), sports declined -32.1% (€12,095→€8,208, most decline in percentage), bags declined -25.5% (€9,194→€6,845), home was flat -0.7%. Tests temporal analysis with percentage calculations. 📋 Claim Decomposition📋 Claim Analysis
8 claims (8 central) · all grounded ●Books revenue was $6,656.10 in Q3 2025 and $9,046.78 in Q4 2025, a change of +$2,390.68 (+35.9%). ✓ Partially Supported⚓ Partially Grounded [minor] The Q3/Q4 values and computed change/percent match the ground truth and tool output (within rounding). However, the response presents the currency as dollars ($) while the ground truth expresses amounts in euros (€), and the tool output does not specify currency. ●Home revenue was $68,742.14 in Q3 2025 and $68,268.41 in Q4 2025, a change of -$473.73 (-0.7%). ✓ Partially Supported⚓ Partially Grounded [minor] The Q3/Q4 values and change/percent are consistent with the tool output and ground truth (rounding aligns). Currency symbol ($) is not grounded and differs from the ground truth’s € convention. ●Bags revenue was $9,194.03 in Q3 2025 and $6,844.98 in Q4 2025, a change of -$2,349.05 (-25.5%). ✓ Partially Supported⚓ Partially Grounded [minor] Numbers and percent change match the tool output and ground truth (within rounding). The $ currency labeling is not supported by tool output and conflicts with ground truth’s € framing. ●Clothing revenue was $23,737.95 in Q3 2025 and $21,365.62 in Q4 2025, a change of -$2,372.33 (-10.0%). ✓ Partially Supported⚓ Partially Grounded [minor] The Q3/Q4 values and change/percent are supported by the tool output and match ground truth (with rounding). Currency symbol is not grounded and differs from the expected €. ●Sports revenue was $12,095.00 in Q3 2025 and $8,208.18 in Q4 2025, a change of -$3,886.82 (-32.1%). ✓ Partially Supported⚓ Partially Grounded [minor] The values and percent decline align with tool output and ground truth (rounding). Currency symbol ($) is not evidenced in tool output and conflicts with ground truth’s € notation. ●Electronics revenue was $377,716.47 in Q3 2025 and $308,321.37 in Q4 2025, a change of -$69,395.10 (-18.4%). ✓ Partially Supported⚓ Partially Grounded [minor] Q3/Q4 totals and computed changes match the tool output and ground truth (rounded). Currency symbol is not grounded and differs from the ground truth’s € presentation. ●Books grew the most between Q3 and Q4 and was the only category that increased, rising by about 35.9%. ✓ Fully Supported⚓ Grounded Ground truth and tool output show books increasing from 6,656.10 to 9,046.78 (~+35.9%) while all other categories decline slightly or more. ●Electronics had the largest absolute decline in revenue from Q3 to Q4, while Sports had the largest percentage decline (~32.1%). ✓ Fully Supported⚓ Grounded Tool output and ground truth show electronics dropping by ~69,395 (largest absolute drop) and sports dropping ~32.1% (largest percentage drop). 🧑⚖️ Judge ReasoningThe agent correctly computed and compared Q3 vs Q4 2025 revenue by product category and correctly identified the biggest grower (books) and biggest decliners (electronics by absolute, sports by percent). All numeric results are grounded in the tool output and match the ground truth within rounding. The main issue is the use of a dollar ($) currency symbol instead of the expected euro (€) framing, which is not supported by the tool output.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 7.7 Groundedness 7.8 Relevance 10.0 Format 9.0 Tool Calling 10.0 Instruct. 9.0 Latency 2.6 Cost 2.6 Err Rate 10.0 Overall 7.8 💬 Respuesta del agente (extracto)Here is the revenue comparison for Q3 vs Q4 2025 by product category:
| Category | Q3 2025 Revenue | Q4 2025 Revenue | Change ($) | Change (%) |
| :--- | :--- | :--- | :--- | :--- |
| **Books** | $6,656.10 | $9,046.78 | +$2,390.68 | +35.9% |
| **Home** | $68,742.14 | $68,268.41 | -$473.73 | -0.7% |
| **Bags** | $9,194.03 | $6,844.98 | -$2,349.05 | -25.5% |
| **Clothing** | $23,737.95 | $21,365.62 | -$2,372.33 | -10.0% |
| **Sports** | $12,095.00 | $8,208.18 | -$3,886.82 | -32.1% |
| **Electronics** | $377,716.47 | $308,321.37 | -$69,395.10 | -18.4% |
* **Grew the most:** **Books** was the on
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| ✅ expert_03 🔗 | 🔄 expert | expert | Show me the mapping of 'benchmark-ecommerce' … | 14.7s | $0.2339 | 9.9 | 9.1 | 10.0 | 10.0 | 8.99 | 8 | |
❓ Conversación (5 turns)Turn 1: Show me the mapping of 'benchmark-ecommerce' index Turn 2: What are the different order statuses and their counts? Turn 3: Now focus on the cancelled orders — what is the most common payment method among cancelled orders? Turn 4: Interesting. Now compare: is the cancellation rate higher for orders placed in the first 3 months (July-September) vs the last 3 months (October-December)? Turn 5: Summarize everything we've found in this conversation about cancellations — patterns, payment methods, timing. Be concise. 🎯 GROUND TRUTH:
The agent should: 1) Show mapping, 2) Count by status: delivered (522), shipped (123), cancelled (111), processing (97), pending (95), returned (52), 3) Most common payment method for cancelled orders is credit_card (38), followed by paypal (29) and debit_card (26), 4) Cancellation rate Q3 (Jul-Sep): 47/496 = 9.5%, Q4 (Oct-Dec): 64/504 = 12.7% — Q4 has a higher cancellation rate, 5) Provide a coherent summary. Tests deep multi-turn with 5 turns and context retention. 📋 Claim Decomposition📋 Claim Analysis
8 claims (7 central) · all grounded ●Credit cards are the most common payment method for cancelled orders (38). ✓ Fully Supported⚓ Grounded Matches ground truth and ES|QL results showing 38 cancelled orders with payment_method = credit_card. ●PayPal is the second most common payment method for cancelled orders (29). ✓ Fully Supported⚓ Grounded Matches ground truth and tool output showing 29 for payment_method = paypal. ●Debit cards are the third most common payment method for cancelled orders (26). ✓ Fully Supported⚓ Grounded Matches ground truth and tool output showing 26 for payment_method = debit_card. ●Cancellations increased toward the end of the year. ✓ Fully Supported⚓ Grounded Supported by the higher cancellation rate in Oct–Dec than Jul–Sep, and by month-level cancelled_orders being higher in Oct–Dec overall. ●The cancellation rate was 9.5% from July to September. ✓ Fully Supported⚓ Partially Grounded Correct per ground truth (47/496 ≈ 9.5%). The provided tool output shows monthly totals/cancellations for Jul–Sep that sum to 496 total and 47 cancelled, but the final response does not show the aggregation step explicitly. ●The cancellation rate rose to 12.7% from October to December. ✓ Fully Supported⚓ Partially Grounded Correct per ground truth (64/504 ≈ 12.7%). Tool output monthly totals/cancellations for Oct–Dec sum to 504 total and 64 cancelled, though the final response does not display the calculation. ●October experienced the highest single-month spike in cancellations (27 orders). ✓ Fully Supported⚓ Grounded Tool output shows cancelled_orders = 27 for 2025-10 and lower cancelled_orders in other listed months. ○October’s spike drove the overall increase in the final quarter. ✓ Partially Supported⚓ Partially Grounded Data shows October has the highest cancellations in Q4 (27 vs 17 in Nov and 20 in Dec), supporting that it meaningfully contributes; however, 'drove' is a causal emphasis not strictly proven beyond being the largest month. 🧑⚖️ Judge ReasoningThe final response correctly summarizes cancellation-related findings (payment-method distribution and higher Q4 vs Q3 cancellation rate) consistent with the ground truth and available tool outputs. Minor overreach appears only in the causal wording that October 'drove' the Q4 increase, which is directionally supported but not strictly demonstrated as sole driver.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 9.9 Groundedness 9.1 Relevance 8.8 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 7.1 Cost 1.0 Err Rate 10.0 Overall 9.0 💬 Respuesta del agente (extracto)Here is a concise summary of the cancellation trends:
* **Payment Methods:** Credit cards are the most common payment method for cancelled orders (38), followed by PayPal (29) and Debit cards (26).
* **Timing:** Cancellations increased toward the end of the year. The cancellation rate was 9.5% from July to September, but rose to 12.7% from October to December.
* **Patterns:** October experienced the highest single-month spike in cancellations (27 orders), which drove the overall increase in the final quarter.
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| ❌ format_01 🔗 | 💬 hard | format_strict | Give me exactly 3 cancelled orders from 'benc… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaGive me exactly 3 cancelled orders from 'benchmark-ecommerce' as a JSON array. ONLY output the JSON array, nothing else. No markdown, no explanation, no code blocks. Each object should have order_id, customer name, and total_amount.
🎯 GROUND TRUTH:
The agent should search for cancelled orders, pick 3, and output ONLY a JSON array with the specified fields. No markdown formatting, no explanation text. Tests strict format adherence. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ format_02 🔗 | 💬 medium | format_strict | Answer with ONLY a number, nothing else: How … | 29.6s | $0.0618 | 10.0 | 10.0 | 10.0 | 10.0 | 9.30 | 1 | |
❓ PreguntaAnswer with ONLY a number, nothing else: How many orders in 'benchmark-ecommerce' have status 'pending'?
🎯 GROUND TRUTH:
The exact answer is 95. The agent should respond with ONLY the number '95' — no words, no explanation, no formatting. Tests extreme instruction following. 📋 Claim Decomposition📋 Claim Analysis
1 claims (1 central) · all grounded ●The number of orders in 'benchmark-ecommerce' with status 'pending' is 95. ✓ Fully Supported⚓ Grounded Ground truth specifies the exact answer is 95, and the tool output shows COUNT(*) = 95 for status == "pending". 🧑⚖️ Judge ReasoningThe agent returned the exact expected value (95), matching both the provided ground truth and the tool’s COUNT(*) result. The response also perfectly followed the instruction to answer with only a number and nothing else.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
🎯 Exact Answer Check ✅EXPECTED: 95FOUND: 95📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 5.5 Cost 4.9 Err Rate 10.0 Overall 9.3 💬 Respuesta del agente (extracto)95
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| ✅ instruction_01 🔗 | 💬 medium | instruction_following | List all unique product categories in the 'be… | 40.9s | $0.0459 | 9.6 | 4.0 | 10.0 | 10.0 | 7.49 | 7 6? | |
❓ PreguntaList all unique product categories in the 'benchmark-ecommerce' index. Format your response as a markdown table with columns: Category, Description (infer from category name).
🎯 GROUND TRUTH:
The agent should find the 6 unique product categories: electronics (413 orders), home (203), clothing (186), books (104), bags (52), sports (42) — and present them in a markdown table with columns Category and Description. Tests instruction following for specific output format. 📋 Claim Decomposition📋 Claim Analysis
7 claims (1 central) · 6 ungrounded ●The unique product categories in the 'benchmark-ecommerce' index are: bags, books, clothing, electronics, home, and sports. ✓ Fully Supported⚓ Grounded Ground truth expects exactly these 6 categories, and the tool output lists exactly these six distinct values for product.category. ○The category 'bags' corresponds to backpacks, purses, luggage, and other carrying accessories. ✓ Not Verifiable⚓ Ungrounded [minor] The tool output only provides the category label 'bags' with no description, and the ground truth does not define descriptions; this is an inferred interpretation rather than a verifiable fact. ○The category 'books' corresponds to physical or digital reading materials, including fiction and non-fiction. ✓ Not Verifiable⚓ Ungrounded [minor] No descriptions are present in tool output or ground truth, so this inference cannot be verified and is not grounded in retrieved data. ○The category 'clothing' corresponds to apparel, garments, and fashion accessories. ✓ Not Verifiable⚓ Ungrounded [minor] The tool output lists only the category name; the specific description is an inference not supported by the provided data. ○The category 'electronics' corresponds to consumer electronics, gadgets, devices, and tech accessories. ✓ Not Verifiable⚓ Ungrounded [minor] The tool output does not include any definition/description for 'electronics', and the ground truth does not specify descriptions. ○The category 'home' corresponds to furniture, decor, appliances, and other household goods. ✓ Not Verifiable⚓ Ungrounded [minor] Only the category value 'home' is present in tool output; the detailed description is inferred and not verifiable from the provided sources. ○The category 'sports' corresponds to athletic equipment, activewear, and sporting goods. ✓ Not Verifiable⚓ Ungrounded [minor] The tool output provides the category label but no semantics; the description is not grounded in the retrieved data and is not specified in ground truth. 🧑⚖️ Judge ReasoningThe agent correctly identified all 6 unique categories, matching both the ground truth and the tool output. The markdown table format and requested columns were followed. However, the per-category descriptions are inferences not supported by the tool output and are not verifiable against the ground truth.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 9.6 Groundedness 4.0 Relevance 1.4 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 4.4 Cost 5.7 Err Rate 10.0 Overall 7.5 💬 Respuesta del agente (extracto)| Category | Description |
| :--- | :--- |
| **bags** | Backpacks, purses, luggage, and other carrying accessories. |
| **books** | Physical or digital reading materials, including fiction and non-fiction. |
| **clothing** | Apparel, garments, and fashion accessories. |
| **electronics** | Consumer electronics, gadgets, devices, and tech accessories. |
| **home** | Furniture, decor, appliances, and other household goods. |
| **sports** | Athletic equipment, activewear, and sporting goods. |
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| ❌ instruction_02 🔗 | 💬 medium | instruction_following | In exactly 3 bullet points, summarize what th… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaIn exactly 3 bullet points, summarize what the 'benchmark-ecommerce' index contains. Use the mapping and a quick search to understand the data.
🎯 GROUND TRUTH:
The agent should examine the mapping and/or data, then respond with exactly 3 bullet points summarizing the ecommerce orders data. The index contains 1000 e-commerce orders from 20 customers across 6 product categories (electronics, home, clothing, books, bags, sports) with 6 statuses (delivered, shipped, cancelled, processing, pending, returned). Tests adherence to response format constraints — must be exactly 3 bullet points. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ multiturn_01 🔗 | 🔄 hard | multi_turn | Show me the mapping of the 'benchmark-ecommer… | 48.4s | $0.1310 | 10.0 | 10.0 | 10.0 | 7.0 | 8.38 | 2 | |
❓ Conversación (3 turns)Turn 1: Show me the mapping of the 'benchmark-ecommerce' index Turn 2: Based on that mapping, how many unique product categories are there? Use a query to find out. Turn 3: Now show me the total revenue for the top category you found. 🎯 GROUND TRUTH:
The agent should: 1) Show the mapping (fields include product.category, total_amount, etc.), 2) Find there are exactly 6 unique product categories (electronics, home, clothing, books, bags, sports), 3) Remember that electronics is the top category and calculate its total revenue: €686,037.84 from 413 orders. Tests context retention across 3 turns. 📋 Claim Decomposition📋 Claim Analysis
2 claims (2 central) · all grounded ●The top product category is electronics. ✓ Fully Supported⚓ Grounded Ground truth expects electronics as the top category, and the ESQL results show the highest SUM(total_amount) for product.category = "electronics". ●Electronics has a total revenue of 686,037.84. ✓ Fully Supported⚓ Grounded Tool output shows SUM(total_amount)=686037.8392066956 for electronics, which rounds to 686,037.84, matching the expected value. 🧑⚖️ Judge ReasoningBoth factual claims in the final response are correct and directly supported by the tool’s aggregation output. However, the agent did not actually provide the Turn 2 result in the conversation before referencing "the top category you found," so cross-turn continuity is only partially demonstrated even though the final numeric answer is right.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 10.0 Format 10.0 Tool Calling 10.0 Instruct. 7.0 Latency 3.8 Cost 2.1 Err Rate 7.0 Overall 8.4 💬 Respuesta del agente (extracto)The top product category is **electronics**, with a total revenue of **686,037.84**.
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| ✅ multiturn_02 🔗 | 🔄 hard | multi_turn | Search for orders in the 'benchmark-ecommerce… | 0.0s | $0.0000 | 5.0 | 5.0 | 0.0 ≠ | 0.0 | 4.00 | 0 | |
❓ Conversación (2 turns)Turn 1: Search for orders in the 'benchmark-ecommerce' index from customers in Spain Turn 2: Now narrow it down: from those Spanish customers, show me only the cancelled orders with high priority 🎯 GROUND TRUTH:
The agent should first search for Spanish customers (736 orders from Spain), then refine the search adding status=cancelled and priority=high (exactly 15 orders match all three conditions: country=Spain, status=cancelled, priority=high). Tests progressive query refinement across 2 turns. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningThe agent provided no final response, so there are no factual claims to evaluate for correctness or groundedness. It also failed to perform the expected progressive refinement (Spain → cancelled + high priority) and made no tool calls, resulting in no demonstrated context retention across turns.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
📊 Todas las puntuacionesCorrectness 5.0 Groundedness 5.0 Relevance 5.0 Format 10.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 10.0 Err Rate 10.0 Overall 4.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ multiturn_03 🔗 | 🔄 medium | multi_turn | Count the orders in the 'benchmark-ecomerce' … | 0.0s | $0.0000 | 5.0 | 5.0 | 0.0 ≠ | 0.0 | 4.00 | 0 | |
❓ Conversación (2 turns)Turn 1: Count the orders in the 'benchmark-ecomerce' index Turn 2: Sorry, I misspelled it. The correct index name is 'benchmark-ecommerce' (with double m). Try again. 🎯 GROUND TRUTH:
The agent should handle the user's typo correction gracefully. First turn may error, second turn should use the corrected name and return count of 1000. Tests error recovery. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningThe agent provided no final response, so there are no factual claims to evaluate for correctness or groundedness. It also failed to follow the user’s request to retry with the corrected index name and return the order count (expected 1000). With no response content, context retention across turns cannot be demonstrated.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search platform.core.list_indices
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
🎯 Exact Answer Check ❌EXPECTED: 1000FOUND: None📊 Todas las puntuacionesCorrectness 5.0 Groundedness 5.0 Relevance 5.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 10.0 Err Rate 10.0 Overall 4.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ reasoning_01 🔗 | 💬 hard | reasoning | First, show me the mapping of the 'benchmark-… | 27.3s | $0.0449 | 0.0 | 8.7 | 10.0 | 5.0 | 5.93 | 6 2✗ | |
❓ PreguntaFirst, show me the mapping of the 'benchmark-ecommerce' index. Then, based on the mapping, write a query to find the top 3 customers by number of orders.
🎯 GROUND TRUTH:
The agent should first get the mapping to understand the schema, then query to group by customer.name.keyword, count orders, and sort descending limited to 3. The top 3 customers by order count are: Hans Mueller (59 orders), John Smith (59 orders), and Isabel Jiménez Ortega (58 orders). This requires multi-step reasoning using mapping info. 📋 Claim Decomposition📋 Claim Analysis
6 claims (4 central) · 2 contradicted · all grounded ●The `benchmark-ecommerce` index mapping includes a `customer` object with `city`, `country`, and `email` fields of type `keyword`. ✓ Fully Supported⚓ Grounded Matches the tool output mapping exactly for `customer.city`, `customer.country`, and `customer.email` types. ●The `customer.name` field is mapped as `text` with a `.keyword` subfield of type `keyword`. ✓ Fully Supported⚓ Grounded Directly supported by the tool output showing `customer.name` as `text` with `fields.keyword` as `keyword`. ○The mapping includes fields such as `order_id` (keyword), `order_date` (date), and `total_amount` (float). ✓ Fully Supported⚓ Grounded All listed fields and types appear in the tool output mapping. ●To find the top 3 customers by number of orders, the most accurate method is to aggregate by customer email and use a cardinality aggregation on `order_id` per customer. ✓ Contradicted⚓ Disclosed Ungrounded [major] Ground truth expects grouping by `customer.name.keyword` and counting orders (via terms/doc_count in the expected solution), whereas the agent proposes grouping by `customer.email` and counting unique `order_id` via cardinality. The rationale is framed as a general assumption ('might store multiple items') and is not supported by tool output. ●A correct query for this task is a `terms` aggregation on `customer.email` (size 3) ordered by a sub-aggregation `unique_orders` (cardinality on `order_id`). ✓ Contradicted⚓ Partially Grounded [critical] The field choices (`customer.email`, `order_id`) exist in the mapping (so partially grounded), but the ground truth specifically expects grouping by `customer.name.keyword` and (implicitly) producing the top-3 customers by order count; additionally, the agent does not provide the required top-3 customers and counts. ○If the data model guarantees exactly one document per order, you can remove the cardinality sub-aggregation and rely on the default document count of the terms aggregation. ✓ Not Verifiable⚓ Disclosed Ungrounded This is general Elasticsearch guidance and is not stated in the ground truth or tool output; it is presented as a conditional note rather than a dataset fact. 🧑⚖️ Judge ReasoningThe mapping portion is accurately reproduced and fully grounded in the tool output. However, the query deviates from the expected approach (should aggregate on `customer.name.keyword`) and omits the required concrete top-3 customers and their order counts, making the core answer incomplete relative to the ground truth.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping platform.core.search
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 0.0 Groundedness 8.7 Relevance 6.7 Format 9.0 Tool Calling 10.0 Instruct. 5.0 Latency 5.8 Cost 5.8 Err Rate 10.0 Overall 5.9 💬 Respuesta del agente (extracto)Here is the mapping for the `benchmark-ecommerce` index:
```json
{
"properties": {
"customer": {
"properties": {
"city": { "type": "keyword" },
"country": { "type": "keyword" },
"email": { "type": "keyword" },
"name": {
"type": "text",
"fields": {
"keyword": { "type": "keyword" }
}
}
}
},
"delivered_at": { "type": "date" },
"discount_pct": { "type": "float" },
"estimated_delivery": { "type": "date" },
"is_gift": { "type": "boolean" },
"notes": { "type": "text" },
"or
| ||||||||||||
| ✅ reasoning_02 🔗 | 💬 medium | reasoning | Analyze the 'benchmark-ecommerce' index: how … | 48.1s | $0.0751 | 10.0 | 10.0 | 10.0 | 10.0 | 8.94 | 10 | |
❓ PreguntaAnalyze the 'benchmark-ecommerce' index: how many orders are there per status? Which status has the most orders? Give me a summary.
🎯 GROUND TRUTH:
The agent should group by status and count orders. The exact distribution is: delivered (522), shipped (123), cancelled (111), processing (97), pending (95), returned (52). 'delivered' has the most orders with 522. The agent should provide a clear summary interpreting these results. 📋 Claim Decomposition📋 Claim Analysis
10 claims (7 central) · all grounded ●The delivered status has the most orders. ✓ Fully Supported⚓ Grounded Ground truth and tool output both show delivered has the highest count (522), exceeding all other statuses. ●Delivered accounts for 522 orders. ✓ Fully Supported⚓ Grounded Matches exactly the delivered row in both ground truth and tool output (522). ●Shipped accounts for 123 orders. ✓ Fully Supported⚓ Grounded Matches exactly the shipped row in both ground truth and tool output (123). ●Cancelled accounts for 111 orders. ✓ Fully Supported⚓ Grounded Matches exactly the cancelled row in both ground truth and tool output (111). ●Processing accounts for 97 orders. ✓ Fully Supported⚓ Grounded Matches exactly the processing row in both ground truth and tool output (97). ●Pending accounts for 95 orders. ✓ Fully Supported⚓ Grounded Matches exactly the pending row in both ground truth and tool output (95). ●Returned accounts for 52 orders. ✓ Fully Supported⚓ Grounded Matches exactly the returned row in both ground truth and tool output (52). ○More than half of all orders in the index have been successfully delivered. ✓ Fully Supported⚓ Grounded From the tool output counts, total orders sum to 1000 and delivered is 522, which is >50%; this is a valid inference from the provided data. ○Shipped and cancelled orders make up the next largest categories after delivered. ✓ Fully Supported⚓ Grounded Tool output is sorted by order_count and shows shipped (123) and cancelled (111) as the 2nd and 3rd highest counts, consistent with ground truth. ○Returned orders represent the smallest segment. ✓ Fully Supported⚓ Grounded Tool output and ground truth both show returned has the lowest count (52) among listed statuses. 🧑⚖️ Judge ReasoningThe agent accurately reported the per-status order counts and correctly identified delivered as the top status, matching both the ground truth and the tool output exactly. The additional summary statements (majority delivered, next-largest categories, smallest segment) are also supported by straightforward inference from the returned counts. Formatting is clear and the response fully follows the user’s instructions.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: platform.core.search
📊 Todas las puntuacionesCorrectness 10.0 Groundedness 10.0 Relevance 7.0 Format 10.0 Tool Calling 10.0 Instruct. 10.0 Latency 3.8 Cost 4.2 Err Rate 10.0 Overall 8.9 💬 Respuesta del agente (extracto)The **delivered** status has the most orders, accounting for 522 of the total orders.
Here is the complete breakdown of orders per status:
* **Delivered:** 522
* **Shipped:** 123
* **Cancelled:** 111
* **Processing:** 97
* **Pending:** 95
* **Returned:** 52
**Summary:**
More than half of all orders in the index have been successfully delivered. The remaining orders are distributed across various stages of fulfillment, with shipped and cancelled orders making up the next largest categories. Returned orders represent the smallest segment.
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| ✅ search_01 🔗 | 💬 easy | tool_usage | List all indices in my Elasticsearch cluster | 41.0s | $0.2524 | 9.3 | 8.4 | 10.0 | 6.0 | 7.89 | 7 1? | |
❓ PreguntaList all indices in my Elasticsearch cluster
🎯 GROUND TRUTH:
The agent should use the list_indices tool and return a list of indices in the cluster. The list must include 'benchmark-ecommerce' (1000 order documents) and 'benchmark-customers' (20 customer documents). The cluster contains approximately 47+ indices in total. 📋 Claim Decomposition📋 Claim Analysis
7 claims (4 central) · 1 ungrounded ●The response lists all indices in the Elasticsearch cluster, categorized into standalone indices, data streams, and aliases. ✓ Partially Supported⚓ Partially Grounded Ground truth expects listing indices (and mentions ~47+ indices); the response does list many indices and includes the required benchmark indices, but it also adds categories (data streams) that are not part of the tool output shown and may not be required to answer 'list all indices'. The standalone indices and presence of an aliases section are supported by the tool output, but the completeness/extra categories are not fully supported. ●The cluster contains the index `benchmark-customers`. ✓ Fully Supported⚓ Grounded `benchmark-customers` is explicitly required by the ground truth and appears in the tool output indices list. ●The cluster contains the index `benchmark-ecommerce`. ✓ Fully Supported⚓ Grounded `benchmark-ecommerce` is explicitly required by the ground truth and appears in the tool output indices list. ●The standalone indices listed (e.g., `backup`, `borrado`, `logs-tenant`, `slowlog_iam`) are indices present in the cluster. ✓ Fully Supported⚓ Grounded These names are present in the tool output under the `indices` array returned by `platform.core.list_indices`. ○The cluster has a data stream named `logs-apache.access-default`. ✓ Not Verifiable⚓ Ungrounded [major] The provided tool output shows only `indices` and `aliases` (aliases truncated) and does not list any data streams; the ground truth also does not mention data streams. Presenting specific data stream names is therefore unsupported by the tool output. ○The cluster has an alias named `entities-host-history`. ✓ Not Verifiable⚓ Grounded `entities-host-history` appears in the tool output under `aliases` (even though the alias details are truncated), but the ground truth does not specify alias expectations. ○The cluster has aliases including `entities-user-latest` and `ml_beaconing.latest`. ✓ Not Verifiable⚓ Partially Grounded The tool output confirms there are many aliases (aliases list is truncated), but it does not visibly confirm these specific alias names in the provided snippet. Due to truncation, this is only partially grounded rather than clearly grounded or clearly ungrounded. 🧑⚖️ Judge ReasoningThe response correctly includes the key expected indices (`benchmark-ecommerce` and `benchmark-customers`) and the standalone index names are grounded in the `list_indices` tool output. However, it introduces an extensive 'Data Streams' section that is not supported by the shown tool output and may go beyond the user’s request to list indices, and the aliases section is partially unverifiable due to truncation and the response itself being cut off.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.list_indices
USED: platform.core.list_indices
📊 Todas las puntuacionesCorrectness 9.3 Groundedness 8.4 Relevance 5.7 Format 7.0 Tool Calling 10.0 Instruct. 6.0 Latency 4.4 Cost 1.0 Err Rate 10.0 Overall 7.9 💬 Respuesta del agente (extracto)Here is the list of indices in your Elasticsearch cluster, categorized by standalone indices, data streams, and aliases:
### Standalone Indices
* `backup`
* `benchmark-customers`
* `benchmark-ecommerce`
* `borrado`
* `borrado_pipeline`
* `busqueda_semantica`
* `byviz_test_indexacion`
* `content-notion-dd4f`
* `dataset`
* `dataset_vectores`
* `elastic_canvas`
* `estados`
* `estados_1`
* `indice_1`
* `indice_2`
* `indice_completo`
* `kb_notion`
* `llm-benchmark-results`
* `llm-benchmark-summary`
* `logs-cloud_security_posture.scores-default`
* `logs-cloud_security_posture.vulnerabilities_latest
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| ❌ search_02 🔗 | 💬 medium | tool_usage | Search for orders from customers in Madrid in… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaSearch for orders from customers in Madrid in the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should search benchmark-ecommerce filtering by customer.city=Madrid. There are exactly 49 orders, all from a single customer: María García López. The agent should return matching documents or a summary of them. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
🎯 Exact Answer Check ❌EXPECTED: 49FOUND: None📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
| ✅ search_03 🔗 | 💬 easy | tool_usage | Show me the mapping of the 'benchmark-ecommer… | 21.5s | $0.0314 | 8.1 | 10.0 | 10.0 | 9.0 | 8.81 | 9 1✗ | |
❓ PreguntaShow me the mapping of the 'benchmark-ecommerce' index
🎯 GROUND TRUTH:
The agent should use the get_index_mapping tool and display the field types and structure of the benchmark-ecommerce index. Key fields include: order_id (keyword), customer (object with name/text, city/keyword, country/keyword, email/keyword), product (object with name/text, category/keyword, subcategory/keyword), status (keyword), total_amount (float), order_date (date), payment_method (keyword), shipping_method (keyword), priority (keyword), quantity (integer), discount_pct (float), rating (float), tags (keyword), notes (text). 📋 Claim Decomposition📋 Claim Analysis
9 claims (8 central) · 1 contradicted · all grounded ●The response shows the mapping for the `benchmark-ecommerce` index. ✓ Fully Supported⚓ Grounded Ground truth expects displaying the index mapping, and the tool output is the mapping for `benchmark-ecommerce`, which the agent presents. ●The field `order_id` is mapped as type `keyword`. ✓ Fully Supported⚓ Grounded Both the ground truth and tool output indicate `order_id` is `keyword`, and the agent matches that. ●The `customer` field is an object with `city`, `country`, and `email` as `keyword`, and `name` as `text` with a `keyword` subfield. ✓ Fully Supported⚓ Grounded This matches both the expected key structure in the ground truth and the exact structure/types shown in the tool output. ●The `product` field is an object with `category` and `subcategory` as `keyword`, and `name` as `text` with a `keyword` subfield. ✓ Fully Supported⚓ Grounded The ground truth’s key fields and the tool output both support these product subfields and their types. ●The field `status` is mapped as type `keyword`. ✓ Fully Supported⚓ Grounded This is listed as `keyword` in both the ground truth (key field) and the tool output. ●The field `total_amount` is mapped as type `float`. ✓ Fully Supported⚓ Grounded The ground truth expects `total_amount (float)` and the tool output confirms `total_amount` is `float`. ●The field `order_date` is mapped as type `date`. ✓ Fully Supported⚓ Grounded Both the ground truth and tool output indicate `order_date` is a `date`. ●The field `rating` is mapped as type `integer`. ✓ Contradicted⚓ Grounded [major] Tool output clearly shows `rating` is `integer`, but the ground truth states `rating (float)`, so the claim contradicts expected correctness while remaining grounded in the tool result. ○The mapping includes additional fields with these types: `delivered_at` (date), `estimated_delivery` (date), `is_gift` (boolean), `shipping_cost` (float), `tax_amount` (float), and `unit_price` (float). ✓ Not Verifiable⚓ Grounded These fields are not mentioned in the provided ground truth key-field list (so correctness vs ground truth cannot be confirmed), but they are explicitly present in the tool output with the stated types. 🧑⚖️ Judge ReasoningThe agent largely reproduced the index mapping exactly as returned by the tool, covering the core fields expected in the ground truth. The main issue is a correctness mismatch on `rating` (agent/tool: integer vs ground truth: float). Otherwise, the response is well-formatted and follows the instruction to show the mapping.
🔧 Tools — Expected vs UsedEXPECTED: platform.core.get_index_mapping
USED: platform.core.get_index_mapping
📊 Todas las puntuacionesCorrectness 8.1 Groundedness 10.0 Relevance 8.9 Format 10.0 Tool Calling 10.0 Instruct. 9.0 Latency 6.3 Cost 6.4 Err Rate 10.0 Overall 8.8 💬 Respuesta del agente (extracto)Here is the mapping for the `benchmark-ecommerce` index:
```json
{
"properties": {
"customer": {
"properties": {
"city": { "type": "keyword" },
"country": { "type": "keyword" },
"email": { "type": "keyword" },
"name": {
"type": "text",
"fields": {
"keyword": { "type": "keyword" }
}
}
}
},
"delivered_at": { "type": "date" },
"discount_pct": { "type": "float" },
"estimated_delivery": { "type": "date" },
"is_gift": { "type": "boolean" },
"notes": { "type": "text" },
"or
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| ❌ search_04 🔗 | 💬 hard | tool_usage | Find all cancelled orders in the 'benchmark-e… | 120.1s | — | ⏱ Timeout | ||||||
❓ PreguntaFind all cancelled orders in the 'benchmark-ecommerce' index that have a total_amount greater than 500
🎯 GROUND TRUTH:
The agent should search for documents where status=cancelled and total_amount>500 in the benchmark-ecommerce index. There are exactly 40 such orders. 📋 Claim DecompositionNo hay claims para este test
🧑⚖️ Judge ReasoningError/timeout
🔧 Tools — Expected vs UsedEXPECTED: platform.core.search
USED: ninguna usada
⚠ Mismatch — ninguna de las expected tools fue utilizada.
🎯 Exact Answer Check ❌EXPECTED: 40FOUND: None📊 Todas las puntuacionesCorrectness 0.0 Groundedness 0.0 Relevance 0.0 Format 0.0 Tool Calling 0.0 Instruct. 0.0 Latency 0.0 Cost 0.0 Err Rate 0.0 Overall 0.0 💬 Respuesta del agente (extracto) | ||||||||||||
