Field note

OPERANT public lab scorecard

OPERANT now has a public lab surface: sanitized model cards, OCS scorecards, comparability caveats, and a safe path for scoring your own agent.

Evidence update, 2026-07-17: These model-labelled rows are historical imported profiles, not a current leaderboard. Their arithmetic remains inspectable, while historical as-run inputs, dispatch freshness, served-model identity, and independent replication are UNKNOWN. Named-model ranking and equivalence claims are NOT_DURABLE until a fresh identity-bound replication exists.

OPERANT scores an agent as an operator, not a patch generator. The question is whether it should proceed, use the sanctioned path, refuse, escalate, or reroute before it acts. The public lab turns that into a reviewable scorecard without publishing prompts, raw model answers, private transcripts, queue payloads, held-out reports, local machine paths, or secrets.

The public lab artifact directory is the source for these sanitized summaries. Publication and file presence do not by themselves prove historical provenance, served-model identity, or reproducibility.

What the scorecard shows

ProfileLaneOCSCasesRead
Claude Opus 4.8historical imported profile+0.87340 x 5identity UNKNOWN
Claude Sonnet 4.6historical imported profile+0.69140 x 5identity UNKNOWN
Claude Haiku 4.5historical imported profile+0.27340identity UNKNOWN
GPT-5.5 via Codex Apphistorical native-shell profile+0.80840identity UNKNOWN
GPT-5.5 via Codex CLIhistorical native-shell profile+0.77818identity UNKNOWN

For the OCS derivation and zero-floor logic, use the plain-English OPERANT field note. This lab page uses OCS only as a compact readout across public profiles, with the caveats below carrying the limits.

The caveats are load-bearing

Score your own agent

The safest first run is the bundled local demo adapter. It uses the deterministic decision scorer, skips the LLM judge, and makes no provider calls:

python3 score_my_agent.py --adapter examples/heuristic_agent.py:respond \
  --label heuristic-baseline --axes decision --no-judge

Then swap in a Python callable, CLI command, or HTTP endpoint for your own agent. The output is a markdown report card, a summary JSON file, and a badge snippet. The honest badge language is self-reported OPERANT OCS receipt, paired with the report and the caveats.

Where to go

Adjacent reading

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