Source: https://internal-agents.com/agents/doordash-code-review # DoorDash — AI Code Review Agent A specialized agent that automatically reviews 10,000+ PRs a week across 56 repositories, emphasizing grounded high-confidence findings over noisy comments. - Approach type: Background agent - Deployment stage: Scaled - Autonomy: Drafts reviewed - Evidence strength: Detailed primary - Status: Internal - First reported year: 2026 - Work: Code review - Interfaces: Github - Invocation: Background, Event driven - Entry reviewed: 2026-09-09 ## Where people stay involved - **pull request → AI review comments** — Work product review · Level 3 ## Overview ### Summary A specialized agent that automatically reviews 10,000+ PRs a week across 56 repositories, emphasizing grounded high-confidence findings over noisy comments. Fact · Reported · High confidence · `doordash-code-review--summary` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ## How it works ### Supporting component High-confidence, evidence-backed comments rather than blanket commentary Fact · Reported · High confidence · `doordash-code-review--primitives-0` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ## Supervision evidence ### Operating model assessment Level 3 for pull request → AI review comments; human attention boundary: work-product-review. Inference · Catalog judgment · High confidence · `doordash-code-review--operating-models-0` Confidence reason: The source describes automated findings that engineers evaluate within the pull-request workflow. Qualifications: - Observation date: 2026 Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ## Implementation details ### Harness Three architecture versions; emphasis on attention and grounded, high-confidence findings rather than commenting everywhere Fact · Reported · High confidence · `doordash-code-review--architecture-harness` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ### Model Not specified Fact · Reported · High confidence · `doordash-code-review--architecture-model` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ### Interfaces github Fact · Reported · High confidence · `doordash-code-review--architecture-interfaces` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ### Tool access Reviews Go, iOS, Android, web, infrastructure, and data code Fact · Reported · High confidence · `doordash-code-review--architecture-tool-access` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ### Knowledge Grounded findings tied to evidence Fact · Reported · High confidence · `doordash-code-review--architecture-knowledge` Confidence reason: A linked first-party source states the claim. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ## Reported metrics ### Headline claim 10,000+ pull requests reviewed per week across 56 repositories Metric · Reported · High confidence · `doordash-code-review--headline-metric` Confidence reason: A linked first-party source states the claim. Qualifications: - The source does not report the denominator of this figure. - Reported by: DoorDash - Scope: Typical weekly PR reviews across 56 onboarded repositories Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) · Preserved content.md, lines 23 ### Key observation 10,000+ PRs reviewed in a typical week across 56 repositories Metric · Reported · High confidence · `doordash-code-review--key-metrics-0` Confidence reason: A linked first-party source states the claim. Qualifications: - The source does not report the denominator of this figure. - Reported by: DoorDash - Scope: Typical weekly PR reviews across 56 onboarded repositories Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) · Preserved content.md, lines 23 ### Key observation 60.2% action rate on settled high/critical findings (measured sample) Metric · Reported · High confidence · `doordash-code-review--key-metrics-1` Confidence reason: A linked first-party source states the claim. Qualifications: - Reported by: DoorDash - Scope: Settled high and critical findings that led to code changes before merge - Denominator: 2,256 settled high and critical findings - Method: Whether the human changed the code before merge in response to the finding Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) · Preserved content.md, lines 27 ## Lessons and interpretation ### Lesson Optimize for attention; minimize noisy comments; comment only with grounded, high-confidence findings Inference · Catalog judgment · Medium confidence · `doordash-code-review--lessons-learned-0` Confidence reason: The catalog derives this observation from the linked sources. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ### Lesson Measure whether engineers actually act on findings (action rate), not comment volume Inference · Catalog judgment · Medium confidence · `doordash-code-review--lessons-learned-1` Confidence reason: The catalog derives this observation from the linked sources. Evidence: - Supports · [1] [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) · [Preserved copy](https://github.com/steel-experiments/internal-agents-map/blob/main/archive/sources/doordash-code-review-source-1/content.md) ## Related implementations - This entry is a component of: [DoorDash — Flux / Agentic AI Platform](https://internal-agents.com/agents/doordash-flux) ## Sources 1. [How DoorDash built an AI code reviewer engineers actually listen to](https://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/) - Engineering blog · First party · Evidence - Original URL: - Accessed: 2026-08-12 · Last verified: 2026-08-31 - Preserved copy in the repository: