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DoorDash · Background agent

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.

1

Approach type
Background agent
Work
Code review
Human involvement
Drafts reviewed
Invocation
Background, Event driven
Interfaces
Github
Deployment stage
Scaled
Evidence strength
Detailed primary
Entry reviewed

How it works

The workflow the sources report for this implementation.

High-confidence, evidence-backed comments rather than blanket commentary

1

Where people stay involved

  • pull request → AI review commentsWork product review · Level 3

Level 3 for pull request → AI review comments; human attention boundary: work-product-review.

1

Observation date
2026

Implementation details

Harness

Three architecture versions; emphasis on attention and grounded, high-confidence findings rather than commenting everywhere

1

Model

Not specified

1

Interfaces

github

1

Tool access

Reviews Go, iOS, Android, web, infrastructure, and data code

1

Knowledge

Grounded findings tied to evidence

1

Reported results and limitations

The catalog records what the sources report, with the scope and the denominator of every figure. A qualification below limits the figure it sits under.

Reported metrics

Headline claim

10,000+ pull requests reviewed per week across 56 repositories

1

The source does not report the denominator of this figure.

Reported by
DoorDash
Scope
Typical weekly PR reviews across 56 onboarded repositories
Key observation

10,000+ PRs reviewed in a typical week across 56 repositories

1

The source does not report the denominator of this figure.

Reported by
DoorDash
Scope
Typical weekly PR reviews across 56 onboarded repositories
Key observation

60.2% action rate on settled high/critical findings (measured sample)

1

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

Lessons and interpretation

Optimize for attention; minimize noisy comments; comment only with grounded, high-confidence findings

1

Measure whether engineers actually act on findings (action rate), not comment volume

1

Sources and research details

Citations link to the original publisher. Each source also keeps a preserved copy in the repository, so a changed or removed page stays checkable.

  1. How DoorDash built an AI code reviewer engineers actually listen tohttps://careersatdoordash.com/blog/doordash-built-an-ai-code-reviewer-engineers-actually-listen-to/Engineering blog · First party · Last source verification: 2026-08-31
    Preserved copy ↗
Research details for every claim on this page
  1. Summary
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  2. Headline claim
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
    Reported by
    DoorDash
    Scope
    Typical weekly PR reviews across 56 onboarded repositories
    Denominator
    Not reported
    Method
    Not reported
    Observation date
    Not reported
  3. Harness
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  4. Model
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  5. Interfaces
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  6. Tool access
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  7. Knowledge
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  8. Supporting component
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  9. Key observation
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
    Reported by
    DoorDash
    Scope
    Typical weekly PR reviews across 56 onboarded repositories
    Denominator
    Not reported
    Method
    Not reported
    Observation date
    Not reported
  10. Key observation
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
    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
    Observation date
    Not reported
  11. Lesson
    Statement type
    Inference
    Provenance
    Catalog judgment
    Confidence
    Medium
    Confidence reason
    The catalog derives this observation from the linked sources.
  12. Lesson
    Statement type
    Inference
    Provenance
    Catalog judgment
    Confidence
    Medium
    Confidence reason
    The catalog derives this observation from the linked sources.
  13. Operating model assessment
    Statement type
    Inference
    Provenance
    Catalog judgment
    Confidence
    High
    Confidence reason
    The source describes automated findings that engineers evaluate within the pull-request workflow.
    Observation date
    2026
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