How to Review AI-Generated Pull Requests at Scale (Best Practices for 2026)

AI-generated pull requests can appear polished and convincing while containing subtle errors such as nonexistent APIs, incomplete implementations, unnecessary dependencies, or weak tests that do not validate meaningful behavior. Effective review should begin by requiring the agent to explain its reasoning, assumptions, and omissions, then examining dependency and lockfile changes before reviewing implementation details. Reviewers are encouraged to compare changes against the original request for completeness, scrutinize tests as carefully as production code, and prioritize deep inspection of high-risk areas such as authentication, payments, data handling, concurrency, and irreversible user-facing behavior. Because reviewing every line is impractical at high AI-generated code volumes, the approach recommends risk-based triage and automating repeatable checks, while retaining human judgment for assessing assumptions, correctness, and potential impact.

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