Codex Iterative Repair Loop (JSON-Schema Review → Repair)

OpenAI's first-party loop recipe: a script alternates a Codex review pass that emits machine-readable findings with a repair pass fed those findings verbatim, looping until validation passes, attempts run out, progress stalls, or a decision needs human review.

Implementation note

When to use: repair work you want structured rather than freeform — a codebase with known issues where you need machine-readable findings driving fixes, with clear stop conditions. This is OpenAI's first-party loop recipe. How it works: a script alternates two codex exec calls. The review pass emits remaining issues as machine-readable findings conforming to a JSON schema; the repair pass is fed those findings verbatim and fixes them. The loop continues while findings remain and halts for exactly one of four reasons: validation passes, max attempts are reached, the remaining delta stops changing between passes, or the next decision needs human review. Safety: the four enumerated stop conditions are the rail — stalled progress and needs-a-human are first-class exits rather than failure modes discovered later, and the attempt cap bounds spend. The JSON-schema findings double as an audit trail of what the loop believed was wrong at each pass.

Source: OpenAI Cookbook ↗

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