AI Code Review Packet: Make Agent-Written Pull Requests Easy to Trust

The challenge of reviewing AI-generated pull requests has become increasingly burdensome for human reviewers. Traditional pull requests hide important contextual information, and AI agents can generate polished code quickly, but the review process suffers as a result. The diff is often larger than anticipated, making it difficult for reviewers to understand the changes, potential risks, and evidence of the work's safety. Reviewers may not know which files the AI agent inspected or what commands were run.

To address this issue, an AI code review packet has been proposed. This structured review artifact, attached to a pull request, provides a summary of the intent, changed surface area, risk level, tests, commands run, evidence, and open questions. The packet helps reviewers quickly understand what changed, what risks were introduced, and what evidence proves the change works. It does not replace the code review process but makes it more efficient and focused.

The AI code review packet consists of seven key sections: Intent, Changed Surface Area, Risk Rating, Evidence, Edge Cases, Rollback Plan, and Open Questions. By providing this structured information, reviewers can quickly assess the risk level and focus their attention on the most critical aspects of the pull request. For AI-assisted engineering, this packet offers more value than a lengthy natural-language summary, as reviewers need clear routing information rather than a lengthy narrative.

The search gap currently exists in the form of numerous content pieces about AI code review tools and AI pair programming. However, the lack of practical guidance on how to review agent-written pull requests is evident. Useful long-tail keywords include "AI code review packet," "agent-written pull request checklist," "AI-generated code review workflow," "pull request evidence template," "AI PR risk assessment," and "agentic coding quality gates."

The unique angle in this context is not to use AI to review code but rather to make AI-written code easier for humans to verify. By implementing a consistent structure for AI code review packets, teams can ensure that every agent-written pull request carries the same shape of proof, making it easier for reviewers to navigate and assess the work.

Adding a risk score before reviewing the PR can help prioritize the workload based on the potential impact and complexity of the changes. This approach aims to stop risky changes from being perceived as routine, ensuring that the review process remains focused and efficient.