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How AI Agent Pull Requests Should Be Structured for Safe Code Review

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As AI agents increasingly author code changes, reviewers need structured evidence to evaluate scope, assumptions, and risks before approving a merge. A proposed YAML-based policy framework outlines what an agent-authored pull request must include, such as the tested revision, input fixture versions, test results, and any unresolved assumptions. The policy also restricts agents from modifying sensitive files like CI workflows or infrastructure configs, and bars them from approving their own changes or bypassing merge gates. Critically, a policy file alone is insufficient — the underlying repository permissions, identity checks, and merge controls must all be aligned to enforce it. The author also highlights that reverting code does not automatically undo external side effects, making a recovery plan and a designated recovery owner essential parts of any agent-driven change.

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