I Let AI Handle More of Its Own Review Feedback. Then I Had to Give the Work Boundaries

Description and use

Automating review feedback handling with an AI agent in the AIDD Skeleton repository enables broader investigation, bounded modifications, self‑challenging fixes, and deferred work preservation.

Problem

External reviewers find issues, but letting an AI implementation agent handle them autonomously risks endless task expansion and silent scope creep.

Outcome

The author achieved a balanced AI‑review workflow that allows autonomous investigation while preventing scope creep, ensures corrections are self‑challenged, and preserves deferred work for future action.

Workflow

• 1. Reviewer reports a finding.
• 2. AI investigates beyond the exact location, asking what assumption failed and where else it matters.
• 3. AI determines if the finding is valid and assigns a disposition: Accept now, Reject, Defer, or Observe.
• 4. If accepted, AI applies the fix but treats the correction as a new adversarial surface, challenging it in subsequent reviews.
• 5. If the same issue recurs, AI triggers Structural Reassessment to examine underlying design or invariants.
• 6. Deferred items are stored in workbench/ with reason, context, and reconsideration conditions.
• 7. The process repeats until all findings are resolved or deferred items are revisited.

Evidence

The implementation agent already had the repository context. Ideally, one useful finding should let it investigate further on its own.

Source

The better the agent became at finding related issues, the easier it became for the current task to expand indefinitely.

Source

I stopped treating review findings as isolated lines.

Source