AI Code Review: How to Actually Review Code an Agent Wrote

AI code review combines large language models with static analysis to examine pull-request changes, summarize their effects, assess risk, and flag potential security, logic, and design issues within developers’ existing workflows, while leaving final decisions to human reviewers. Its growing relevance reflects the increased volume of AI-generated code and evidence that such code can contain vulnerabilities, including missing input validation, hardcoded secrets, weak authentication, unsafe dependencies, and subtle architectural changes. Unlike traditional pattern-based tools such as SAST, software composition analysis, and secrets scanning, AI review aims to reason about a change’s intent and broader context, including data flows, access controls, and potential blast radius. Effective systems prioritize findings using reachability analysis to reduce false positives by identifying vulnerabilities that attacker-controlled input can actually reach. Recommended adoption practices include beginning in report-only mode, tuning results for relevance, integrating reviews into pull requests and CI/CD pipelines, routing sensitive changes to appropriate reviewers, and measuring outcomes while retaining human oversight. The text presents Endor Labs as one example of a tool using developer, architect, and security perspectives alongside reachability analysis to review security-relevant changes.

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