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 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).

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 question set — the whole set, or a single question file — 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, 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. A run can also be scoped to a single file (see Running an eval).

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.

# 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 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 — its File column shows which file defined each question. 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.
• Questions — All questions (the default) or a single question file, to run only that file’s questions. The selector appears only when the selected branch’s questions come from more than one file, and each file option shows how many questions it holds. Switching branches resets it to All questions.
• 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.
Questions	The file the run was scoped to, or All questions.
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. The run’s scope is repeated in the header, next to Questions.

• 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 in full. To iterate faster, split them across agents/eval_questions/*.yml files and scope the run to one file.
• Grading is execution-based on the result set; it does not semantically judge prose answers.