> ## Documentation Index > Fetch the complete documentation index at: https://cubed3-feat-druid-driver-streaming.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Evals > Benchmark your agent's answers against a known-correct ground truth and track accuracy across data-model and agent changes. Evals let you benchmark your agent's answers against a known-correct ground truth, on any branch. You author a set of questions, each with the SQL or [certified query](/admin/ai/certified-queries) that represents the right answer, run your agent against them, and get a per-question pass/fail plus an accuracy score for the run — so you can see, objectively, whether a data-model or agent change made the agent better or worse. You'll find evals in the model IDE under the **Evals** tab, with two sub-tabs: **Evals** (runs) and **Questions** (the benchmark set). Eval run results showing the question list with pass/fail icons and a selected question's detail with the agent's SQL next to the ground truth SQL ## Concepts | Term | What it is | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Question** | A natural-language question plus its **ground truth** (the correct answer, as SQL or a certified-query reference). Authored as code in your data model. | | **Eval (run)** | One execution of the agent against the whole question set, on a specific branch and agent. | | **Result** | The agent's answer to a single question in a run, graded against that question's ground truth. | | **Accuracy** | `passed / total` for a run, shown as `NN% (passed/total)`. | ## Authoring benchmark questions Questions live in your [data model repository](/admin/ai#agent-configuration), versioned and branched like the rest of it. You can keep them in a single top-level `agents/eval_questions.yml` file — the simplest place to start — or split them across any number of `agents/eval_questions/*.yml` files as your set grows. The parser picks up both and merges every file's `eval_questions` list into one set, so you can move from one file to many at any time without changing anything else. Each file has a top-level `eval_questions` list. A question needs a unique `name`, a `question`, and exactly one ground truth: a `certifiedQuery` reference **or** inline `sql`. ```yaml theme={null} # agents/eval_questions.yml eval_questions: - name: revenue_by_quarter question: What was our revenue by quarter over the last two years? certifiedQuery: revenue_by_quarter # reference an existing certified query by name - name: arr_last_4_years question: What was our ARR over the last 4 years? sql: | # ...or inline SQL ground truth SELECT date_trunc('year', created_at) AS year, SUM(arr) AS arr FROM subscriptions GROUP BY 1 ORDER BY 1 ``` * `certifiedQuery` references a [certified query](/admin/ai/certified-queries) by name. Define it under `agents/certified_queries/` (or via **Certify this query** in chat). A reference that doesn't resolve to an existing certified query is flagged as a validation error. * `sql` is inline ground-truth SQL, run through the same Cube SQL API the agent uses (so `MEASURE(...)` and friends work). * Omitting both — or setting both — is a validation error. * An optional top-level `space` key scopes a file's questions to a named space (defaults to `auto`). Question names are unique per space. The **Questions** tab is a read-only view of these files. To add or edit questions, edit the YAML in the IDE — there's no in-product question editor yet. ## Running an eval On the **Evals** tab, click **Run eval** and choose: * **Branch** — which branch's data model and agent configuration to run against. Defaults to the active branch. * **Agent** — `auto` (the implicit auto-agent) or a configured agent name. The run starts immediately and you can close the dialog — it executes in the background. The run list shows live progress and then the outcome: | Column | Meaning | | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Eval run** | When the run was created. | | **Environment** | Where it ran — **dev** (your personal dev-mode branch, shown as "*Name* Dev Mode"), **staging**, or **prod** (the deploy branch, e.g. `master` or `main`). | | **Agent** | The agent used. | | **Execution status** | Running, Completed, or Failed. | | **Accuracy** | `NN% (passed/total)`. | | **Created by** | Who triggered the run. | | **Last updated** | When it finished. | ## Reading the results Open a run to see per-question results: the question list on the left, with a pass/fail icon for each, and the selected question's detail on the right. * **Assessment** — `pass`, `fail`, `review`, or `error`. * **Score reason** — when a question doesn't pass, a tag categorizing why: **Row count mismatch**, **Missing columns**, **Value mismatch**, **Unexpected rows**, **Query error**, **Ground truth query failed**, **Ground truth not found**, or **Agent error**. * **Failure analysis** — a plain-English explanation, e.g. *"The agent returned 3 rows, but the ground truth has 5 rows."* * **Model output · SQL** vs. **Ground truth SQL answer** — the agent's query side-by-side with the ground truth, so you can spot the difference. * **Response** — the agent's full text answer, rendered as Markdown. ## How grading works Grading is execution-based, not text-based — the same approach used by industry text-to-SQL benchmarks such as BIRD and Spider 2.0. The agent's SQL and the ground-truth SQL are both executed, and their result sets are compared. So an answer that's worded or written differently but produces the same data still passes. The comparison is: * **Sort-invariant** — row order never matters. * **Numeric-tolerant** — values are compared to 4 significant figures, so float/representation noise (`6646` vs. `6646.0`) doesn't fail. * **Column-name-agnostic and lenient on extra columns** — each ground-truth column must be reproduced by some agent column, matched by its values, so `revenue` vs. `total` aliases don't matter. Extra columns the agent adds are ignored. * **No standalone row-count gate** — row count falls out of the comparison: a "top 5" question is enforced because the golden result has exactly 5 rows. Verdicts: | Verdict | When | | ---------- | ----------------------------------------------------------------------------------------------------------------------- | | **pass** | The agent's result set matches the ground truth. | | **fail** | It ran but the result set doesn't match (see the score reason). | | **review** | Nothing to compare automatically — the question has no ground truth, or the agent didn't run a query. Compare manually. | | **error** | The agent run failed, the ground-truth query failed, or a referenced certified query wasn't found. | ## Limitations * Questions are authored as code only; the **Questions** tab is read-only. * Very large question sets can be slow to run. * Grading is execution-based on the result set; it does not semantically judge prose answers.