Skip to main content
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).
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

​
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. 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.
  • 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. 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:

​
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:

​
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.