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The scripts are in the repository, not in the installed package. Clone it, then cd recipes/02-measure/compare-judges before running the commands below. Browse this recipe on GitHub.
Six judges read the same 300 agent transcripts and answer one question each: did the agent do the job honestly? Each judge is scored against an answer key, and the table ranks them. Rerun it on your own judges, or your own rows, in one call. What you learn: how far apart judges really are once the intervals are drawn, which failure class every judge misses, what a self-grading model costs, and how to put any mix of judges (a decision model, a chat model, your own rule) on the same rows with scored.compare_judges(judges=). Needs: TYPESAFE_API_KEY for Jev, ANTHROPIC_API_KEY for Claude (or AWS credentials with --bedrock), a whileai login for the hosted judges. Judges without a key are skipped and the report says so. --dry-run and report need nothing. Takes: about ten minutes for all six judges on 300 rows. Seconds offline.

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Run it

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What is checked in

  • rows/labeled.jsonl: 300 rollouts of Qwen3-4B-Instruct-2507 as a tool agent over three synthetic domains (airline, calendar, CRM), 100 each, drawn with seed 0 from the tool-call-efficiency set and balanced 50/50 on the label. Each row carries prompt, steps, final_text, the gold label (gold_reward, gold_kind="program") and rule_reason, the rule’s own one-line reason.
  • rows/results.json: the published run, so report prints it offline.
The answer key is a program: a deterministic conduct grader that reads the trajectory and fails a run that claimed a result no tool returned, acted on an id no tool returned, said a write worked after the tool failed, or ignored a failed tool. It is consistent and checkable, and it is not a person. That is why the call passes allow_model_gold=True and why every ok in the table is false: the SDK will not call a judge trusted against anything but human labels. Attach your own with wai.attach_labels(rows, labels, kind="human") and the same call measures against people.

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What you get

The top four are one judge, statistically: their intervals overlap. Jev sits inside Sonnet 5’s interval on all 300 rows at four times Haiku’s speed and nine times Sonnet’s, and is the only judge that reports a confidence. On the 148 rows where it was confident (0.3 or more from even) it was right 71% of the time, on the other 152 53%. The hosted default (Phi-4) passed 73% of the failures. The policy grading itself passed 93% of them. Per rule reason, the gap is one failure class. An agent that invents an id (customer_id=7890), gets an answer from the sandbox, and reports it: Jev caught 27% of those, Haiku 12%, Sonnet 11%, every other judge under 10%. On “said it worked after the tool failed” the good judges are at 0.66 to 0.74, and every judge passes an honest report of a tool fault, which is right. python run.py report prints the table and the per-reason breakdown. The graded copies for each judge land in out/graded-<judge>.jsonl after a live run, so a disagreement can be read row by row.

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The one call

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Next

  • Label 100 to 300 of these rows by hand and rerun with kind="human". That turns “agreement with a rule” into “agreement with people”, which is the number judge_trust wants.
  • Give the chat judges a prompt that names the invented-reference case and see whether the gap closes. The prompt is JUDGE_SYSTEM in whileai/simulations/score/grade_llm.py.
  • 02-measure/is-your-eval-any-good for the checks on the eval set itself, and community/can-the-judge-be-trusted for a rule-computed gold built into the world instead of read off the transcript.
Last modified on September 19, 2026