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. 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.
  • 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.
  • QuestionsAll 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.
  • Agentauto (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:

Run evals from CI

You can gate a pull request with either the Cube CLI or the public Platform API. In both cases, store the tenant URL and deployment ID as repository variables, store the API key as a secret, and pass the branch under review. The API key needs SchemaUpdate access to start a run and either SchemaRead or SchemaUpdate access to poll it and read its results. This capability is currently in preview. Contact Cube support to activate it for your account.

With the Cube CLI

Set CUBE_CLI_VERSION to the Cube CLI release you have tested, then install that exact version in the job:
The command waits for completion and exits non-zero when the eval run fails, when it produces no graded questions, or when any question has a verdict other than pass. It also fails closed if the API does not confirm a complete result set. When a complete result set is returned, the command writes eval.json before exiting, including on a failed verdict. If results cannot be verified, the step log explains why and the empty output file is removed, so no empty artifact is uploaded. Add --agent NAME to test a configured agent or --file eval_questions/revenue.yml to limit the run to one question file. Pass --json for a machine-readable document containing both the terminal run and its per-question results when the result set is complete.

With the Platform API

If you do not want to install the CLI, call the same public endpoints directly. This example does not retry the POST, because repeating a non-idempotent start request after an ambiguous network failure could create another run. It bounds every GET, retries transient read failures, and applies the same fail-closed checks as the CLI. Because reads are idempotent, it retries connection resets too; a permanent read error such as 401 will also be retried four times before the job fails. Each read writes to a file so curl can discard a partial response before retrying.
The POST body also accepts agentName and questionFile. Omitting the pagination parameters on the results request returns the complete result set; if pageInfo.hasNextPage is anything other than false, do not use that page as a CI verdict. Both recipes require every selected question to return pass. A review verdict, including one caused by missing ground truth, fails the CI gate. Keep questions intended for manual review in a separate file, then use CLI --file or API questionFile to run an automatically gradable file in CI.

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.
  • Assessmentpass, 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 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.