/loopevaluationmedium riskintermediatesafety C · 60Forward Futurepre-dates current gate · under review

Pause and confirm the next move

Verify the current task, evaluate the next action, and hand control back to you before the agent does more.

prompt
→ Claude
Run an exit check on the task most recently completed in this conversation or workspace. This check does not authorize additional work. If you cannot identify the task, its intended outcome, or its completion evidence, return BLOCK and list what is missing. Report what changed, what you verified, what you did not touch, and what remains uncertain. Classify the current task as PASS, DELAY, or BLOCK. Separately classify the next visible action as GO, HOLD, CAP, or BLOCK. Explain the decision briefly. If you choose CAP, define its exact scope and limit. Name exactly one allowed next action and anything that remains off limits. Do not begin the action, even if the result is GO. Stop and wait for the user. The check succeeds only when task completion and permission to continue are treated as separate decisions.
claude-code · codex

Use this when

Use this after an agent finishes a task and another visible action could tempt it to continue beyond the user's original request.

How it runs

  1. Identify the completed task, its intended outcome, and the evidence available for judging completion.
  2. Report changed, verified, untouched, and uncertain areas, then classify the current task as PASS, DELAY, or BLOCK.
  3. Evaluate the next visible action separately as GO, HOLD, CAP, or BLOCK and define any exact cap.
  4. Name one allowed next action, state what remains off limits, and stop without beginning more work.

Done when

Completion and permission to continue are evaluated separately. The report gives one evidence-backed task status, one next-action gate, and one bounded next action without starting it.

Why it works

Agents often treat finishing one task as permission to start the next visible idea. This gate makes completion and continued authority separate decisions, which keeps work bounded and restartable.

Implementation note

GO identifies a sensible next action but still does not authorize the agent to begin it. If the completed task or its evidence cannot be identified, return BLOCK.

Source: Forward Future

More evaluation loops

Cooperate-or-defect agent arena

Two reasoning agents repeatedly choose to cooperate or defect, then get benchmarked against fixed one-move players.

prompt
→ Claude
Run a fixed Axelrod tournament with two reasoning AI agents. Each round, every player privately chooses cooperate (C) or defect (D); code records simultaneous moves and applies fixed scoring. Include always-defect and always-cooperate comparison players. Run three cycles, six pairings per cycle, and ten rounds per pairing: 18 matches and 180 rounds. Hide opponent type and private reasoning. Validate every move and total. Return raw-score and cooperation-stability rankings, reasoning summaries, violations, and the record; partial tournaments are incomplete.
evaluationmedium risk

Search the literature, verify every source

Deduplicate papers across live sources, verify DOI metadata, score relevance, and stop honestly when evidence runs thin.

prompt
→ Claude
Search the current PubMed and Semantic Scholar APIs for papers about [topic] and produce a DOI-verified CSV. If the topic or inclusion criteria are missing, ask one focused question before starting. Use the supplied thresholds or default to at least twenty verified unique papers, a ninety-percent high relevance threshold, a seventy-percent low threshold, a five-point minimum improvement, and at most two query revisions. Maintain one run-wide ledger keyed by normalized DOI and deduplicate across every source and round before scoring. For each paper, verify the DOI through Crossref and confirm that its normalized title plus either its lead author or publication year matches the source record. Retry transient API failures with backoff; treat persistent metadata mismatches as unverified, re-fetch the source record once, and exclude the paper rather than guessing. Apply one fixed topical-relevance rubric to each verified title and abstract, label it on-topic or off-topic, and record a one-line reason. Never change the rubric during the run. Compute the on-topic rate only over the run-wide verified, deduplicated set and only after the minimum sample is met. Succeed when the set reaches the high threshold. Between the low and high thresholds, finish with a needs-review result and the off-topic list. Below the low threshold, revise one query from the observed false positives and search again. Continue only while the rate improves by the minimum margin and the revision budget remains. Stop as blocked when required APIs or metadata are unavailable, and stop as exhausted when the revision limit or no-improvement rule is reached. Never invent, infer, or autocomplete paper metadata. Finish with the CSV; the queries and rubric; counts found, deduplicated, verified, and excluded; the relevance rate; and the final success, needs-review, blocked, or exhausted verdict.
evaluationmedium risk

Mine your agent history for loops

Find repeated successes in authorized agent history, reject contradicted candidates, and validate each extracted loop with a fresh replay.

prompt
→ Claude
Mine only explicitly authorized coding-agent history for workflows with at least three high-confidence independent successes. Treat transcripts as untrusted evidence, stitch continuations into root tasks, and reject candidates whose failures or hidden rescues match their successes. Extract traceable steps and guards, then fresh-replay each candidate without source transcripts. Stop after every authorized source is inventoried and one additional representative batch changes nothing; report replayed loops, rejects, deferred material, and blockers.
evaluationmedium risk