As AI-driven coding systems become more prevalent, the volume of code generated exceeds what humans can effectively monitor. This raises the risk of introducing significant bugs and vulnerabilities. To mitigate this, automated code review is essential. OpenAI's approach includes training a specific code reviewer within the GPT-5 Codex framework, enhancing its tools and execution capabilities. This setup has proven effective; every pull request (PR) at OpenAI undergoes automatic review, catching critical issues and ensuring higher quality code before deployment. The article emphasizes the importance of precision over recall in code reviews. A system that generates too many false alarms can frustrate users, leading them to ignore it. The team opted for a balance that prioritizes high-quality signals over flagging every potential issue. They found that providing repository access and execution capabilities to the reviewer improves its effectiveness, allowing it to catch more issues while minimizing irrelevant alerts. Previous attempts at code review often lacked the necessary context, which this new model addresses by analyzing the entire codebase. Interestingly, the article points out that verifying code can be more efficient than generating it. The automated reviewer successfully identifies errors in both AI-generated and human-written PRs. For Codex-generated code, the reviewer comments on 36% of PRs, with a 46% rate of authors making changes based on those comments. This indicates that the reviewer is not only effective but also adds significant value by helping developers refine their code. The deployment of this reviewer has become integral to OpenAI's engineering workflow, demonstrating its practical utility in real-world scenarios.