DoorDash built a custom AI code review agent that achieves a 60.2% acceptance rate on high and critical findings across 10,000+ weekly PRs. The system evolved through three versions, with the key innovation being a 'lead scout' that identifies suspicious areas before two deep reviewers investigate them — separating noticing from verifying. The architecture uses per-domain review profiles mined from historical PRs, Slack decisions, and incident history rather than generic AGENTS.md files. A precision-over-recall philosophy means the agent posts fewer but higher-quality comments, each anchored to specific lines with evidence. The system also includes a fixer agent that can apply suggested changes directly to PRs via remote VMs. Key engineering lessons include using per-agent soft/hard timeouts to handle stuck agents, measuring cost per successful review rather than token price, and building evals from real past incidents rather than synthetic puzzles.
