---
title: "How DoorDash built an AI code reviewer engineers actually listen to"
url: https://daily.dev/posts/how-doordash-built-an-ai-code-reviewer-engineers-actually-listen-to-psc8auyls
source_url: https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to
type: article
source: "Doordash"
published: 2026-05-11T16:56:55.348Z
updated: 2026-05-11T16:58:16.426Z
tags: ["ai-agents"]
reading_time: 16
upvotes: 0
comments: 0
language: en
---

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# How DoorDash built an AI code reviewer engineers actually listen to

**[Doordash](https://daily.dev/sources/doordash)** · 16 min read · 0 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to>

---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents)

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