auto-optimize-loop

You can automate multi-round review and refinement by invoking an autonomous loop that evaluates, modifies, and re-evaluates a research artifact until it reaches a positive submission-ready status or hits a configured maximum round limit.

An autonomous review loop works with Codex MCP by enforcing configurable thresholds and persistent state to drive automated improvement, capturing comprehensive logs and evidence for traceability across each evaluation and fix cycle.

Yes, you can configure evaluation thresholds and track progress across autonomous review rounds. The loop uses persistent state to monitor progress and enforce safeguards while driving continuous artifact refinement.

The best way to reduce manual review time is to use an autonomous loop that automates the review, fix, and re-evaluation cycles, accelerating improvement cycles while logging results for traceability and claims.

When the autonomous review loop reaches the maximum number of rounds without a positive assessment, it stops automated refinements. It enforces safeguards and logs all results to leave the research artifact in its last evaluated state.