DoorDash builds a multi-agent AI code reviewer with 60% engineer acceptance rate
Most AI code reviewers attached to pull requests get ignored because their comments are noisy and generic; the central challenge is helping an agent focus on the parts of a change that deserve review and stay quiet when it has nothing useful to add.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · PR opened, agent triggered
Many reviews fire automatically when a PR is opened; engineers do not have to ask for one.
Tools used
GitHubSlack
Outcome
The agent reviews more than 10,000 pull requests per week across 56 repositories, with 60.2% of high and critical findings resulting in engineer code changes before merge, at roughly $3 per review compared to publicly priced comparable products at $5 to $20 per review.
What failed first
A previous third-party review agent achieved only a 46% acceptance rate. DoorDash's own v1 specialist agents missed architectural issues, and v2 general-purpose reviewers spread attention too thin, losing real findings.