A 10-minute checklist for reviewing AI-generated pull requests

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AI-written pull requests look polished, but that polish can conceal recurring weaknesses, so they call for a different review approach. The guidance starts with the file list, since agents often wander into unrelated files, reformat code, or add unnecessary dependencies. Tests generated by agents should be checked for whether they verify real behavior and edge cases, and whether they would fail if the logic were wrong. Reviewers should also scrutinize error handling for swallowed exceptions, unbounded retries, and leaked internals, along with basic security issues like injection risks and missing authorization checks. Finally, the PR description should be verified against the actual code, because AI-written summaries can be fluent yet inaccurate.

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