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Evals and judges

Every agent, scored on real work.

A judge reads what an agent did and writes a score with its reasons. An eval is a set of cases and metrics in code; a run of it ranks agents on success, cost and reliability.

Open the evals appSee routing by rating

Three kinds of judge

A score with who gave it.

A rating names its rater: the task gates (the checks a finished task must pass), a model reviewer, or a person. A rule judge needs no model at all.

How work is judgedWhen an agent finishes, a judge reads the record of what it did. The task gates check a coding task's outcome; a model reviewer reads its change; a rule judge scores a robot attempt from its recorded facets with no model. Each writes a score, its precision, confidence and importance, and the reason, as a record beside the work. An eval runs fixed cases with metrics and ranks the agents on a leaderboard.The agent's runits recordsThe task gatesoutcomeA model reviewerreviewA rule judgeno modelevent: ratedscore · precision ·confidence · importanceAn eval's leaderboardsuccess · cost · reliabilityHow work is judgedWhen an agent finishes, a judge reads the record of what it did. The task gates check a coding task's outcome; a model reviewer reads its change; a rule judge scores a robot attempt from its recorded facets with no model. Each writes a score, its precision, confidence and importance, and the reason, as a record beside the work. An eval runs fixed cases with metrics and ranks the agents on a leaderboard.The agent's runits recordsThe task gatesoutcomeA model reviewerreviewA rule judgeno modelevent: ratedscore · precision · confidence · importanceAn eval's leaderboardsuccess · cost · reliability

Case study 1 · our own work, rated

3,397 tasks rated, each with its rater.

The backlog thread's tasks streams hold every rating of its finished tasks, 2026-10-23 to 2026-11-04.

ratings by the task gates, from each task's outcome
2,779
reviews by model agents, 445 of them by one reviewer member
618
mean score of Claude Code's tasks (3,232 ratings)
0.63
mean score of Codex's tasks (165 ratings)
0.39
ProvenanceFile runs/20261023t132208z-0aea (the backlog thread, on this computer)Output case-studies/backlog-thread/stats.jsonTool scripts/sites/case-studies.mjs backlog-threadRan on This computer (dell-gb10-1), reading the run folderWhen

Case study 2 · a judge with no model

734 attempts, 734 critiques.

In the self-improvement loop, agent:feedback-judge reads each attempt's facets (success, push, the tool into the bench, jerk, set-down force) and writes a critique and its tags. No model runs; the same attempt always gets the same words.

See the loop

Simulated

critiques, one per simulated attempt
734
of 50 held-out cubes once the policy trained on the scores
0 → 40
ProvenanceFile cloud://global/worlds/eval-pick-place/world.tsxRun runs/loop-20261029082227Output case-studies/records/stats.jsonTool packages/domain/world/policy-loop.js (judge), scripts/sites/case-studies.mjs recordsRan on This computer (dell-gb10-1)When

Case study 3 · the evals declared

Seven evals, no ranked runs yet.

CommandAGI's account declares seven evals in production: a personal agent, retail floor operations, a robot arm's articulation, an accessibility DOM pilot, computer-use grounding, a platform harness smoke test and market making. Six are frozen and published.

Nothing ranked yet. No agent's run of these evals is on a production leaderboard. The computer-use eval's runs on this computer are on decision models and zero-token agents.

evals, 21 cases in all
7
frozen and published
6
entries on any leaderboard on 2026-11-04
0
ProvenanceFile GET /v1/evals (production)Output case-studies/evals/stats.jsonTool scripts/sites/case-studies.mjs evalsRan on This computer (dell-gb10-1), reading the production API as user-commandagiWhen

Score your agents on your work.

Write an eval as code in the evals app, or rate a finished task in its thread.

Open the evals appSee routing by rating