AI Code Review Agent

A specialized agent that automatically reviews 10,000+ PRs a week across 56 repositories as of May 2026, emphasizing grounded high-confidence findings over noisy comments.

Company: DoorDash
Approach type: Agent
Work: Code review
Human involvement: Drafts reviewed
Invocation: Background, Event-driven
Interfaces: Github
Deployment stage: Scaled
Evidence strength: Detailed primary
Entry reviewed: 2026-10-08

Copy as MD Ask ChatGPT

How it works

Pull request opening → staged review → published finding and optional fix.

Automatic review trigger

Opening a pull request can automatically start the review alongside human reviewers^{[Source 1]}

Lead scout

A scout reads the diff and produces focused investigation leads without trying to verify them^{[Source 1]}

Deep reviewers

Two reviewers investigate the scout's leads and drop the ones that do not hold up^{[Source 1]}

Optional fixer

A person can tag the agent on a review comment; a remote VM applies the requested change and pushes a reviewable commit to the pull request^{[Source 1]}

Where people stay involved

People review, approve, or guide the agent’s work here.

Supervision level
Work-product review
A person reviews the draft or implementation.
Level 3 of 5
- pull request → AI review comments^{[Source 1]}

How supervision levels work

Implementation details

Model

Not reportedThe source says the design is model-agnostic but names no current model.

Harness

Three architecture versions; emphasis on attention and grounded, high-confidence findings rather than commenting everywhere^{[Source 1]}

Sandbox

Not reportedThe remote VM is documented, but its isolation guarantees are not.

Tool access

Reviews Go, iOS, Android, web, infrastructure, and data code^{[Source 1]}

Knowledge

Grounded findings tied to evidence^{[Source 1]}

Context management

Not reported

Credentials

Not reported

Interfaces

github

^{[Source 1]}

Mechanisms

Grounded findings

Filters review candidates for evidence, confidence, and an actionable file-and-line location^{[Source 1]}

Validation and failure handling

Disprove-it pass

Before publication the system tries to falsify each candidate finding and drops claims that do not survive^{[Source 1]}

Evaluation loop

Changes are tested on real past review misses and high-severity incidents before production acceptance data is checked^{[Source 1]}

Reported observations

These results come from the sources. Notes below each result explain what it covers and any reported limits.

Headline claim

10,000+ pull requests reviewed per week across 56 repositories^{[Source 1]}

Reported by: DoorDash
Scope: Typical weekly PR reviews across 56 onboarded repositories as reported in May 2026

The source does not report the denominator of this figure.

Observed in May 2026

About this result

Category: Adoption output
Evidence type: Reported measurement
What it covers: Typical weekly reviews across 56 repositories

Key observation

More than 25,000 automated code reviews each week as of August 2026, counted as review runs rather than pull requests^{[Source 2]}

Reported by: DoorDash
Scope: Automated code reviews per week across DoorDash agent workflows; the count unit is review runs, not distinct pull requests

The source does not report the denominator of this figure.

Observed in August 2026

About this result

Category: Adoption output
Evidence type: Reported measurement
What it covers: Automated code reviews completed per week across DoorDash agent workflows

Key observation

60.2% action rate on settled high/critical findings (measured sample)^{[Source 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

About this result

Category: Effectiveness
Evidence type: Reported measurement
What it covers: Settled high and critical findings that led to code changes

Key observation

Roughly $3 average cost per review^{[Source 1]}

Reported by: DoorDash
Scope: Average cost of the current staged review workflow

The source does not report the denominator of this figure.

About this result

Category: Cost latency
Evidence type: Reported measurement
What it covers: Average cost of the current review workflow

Key observation

Findings posted about 7 minutes after a pull request opens on average^{[Source 1]}

Reported by: DoorDash
Scope: Time from pull-request opening to a posted finding

The source does not report the denominator of this figure.

About this result

Category: Cost latency
Evidence type: Reported measurement
What it covers: Average time from PR opening to a posted finding

Lessons

Reported opinion: DoorDash favors fewer actionable review comments, accepting that this policy will miss some issues.^{[Source 1]}

Catalog interpretation: DoorDash's reported acceptance rate describes how engineers use published findings; it cannot count issues the reviewer missed.^{[Source 1]}

Back to agents

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Preview prompt

Help me update the Internal Agents Map record for AI Code Review Agent at DoorDash.

Repository: https://github.com/steel-experiments/internal-agents-map
Record: data/agents/doordash-code-review.yaml
Published record: https://internal-agents.com/agents/doordash-code-review
Readable context: https://internal-agents.com/agents/doordash-code-review.md

Use the correction or public source I provide in this conversation. If I have not supplied either, ask what I want changed before editing.

Use an existing checkout of this repository, or clone it if needed. Read the current record from the repository. Follow AGENTS.md, CONTRIBUTING.md, and the relevant instructions in data/schema.md.

Verify that the evidence supports the requested change. Preserve uncertainty and distinguish reported facts from catalog judgments. Follow the repository process for capturing new sources. If evidence is insufficient, explain the gap instead of inventing details.

Make a focused change on a branch. Regenerate derived files and run the required checks. Do not edit generated content by hand.

Show me the change, supporting evidence, and check results, including any checks you could not run. Ask whether to submit a pull request unless I already requested one. Do not merge the pull request. If you cannot edit the repository, prepare a source-backed issue draft.

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.

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
Delegating Engineering Work To Cloud-Based Agents (Flux)https://careersatdoordash.com/blog/delegating-engineering-work-to-cloud-based-agents/Engineering blog · First party · Last source verification: 2026-10-08
Preserved copy

Question coverage and scope

purpose: Reported
workflow: Reported
human involvement: Reported
implementation: Reported
validation: Reported
observations: Reported
lessons: Reported

Research details for every claim on this page

Summary
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen to
Headline claim
Statement type
Metric
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
Reported by
DoorDash
Scope
Typical weekly PR reviews across 56 onboarded repositories as reported in May 2026
Denominator
Not reported
Method
Not reported
Observation date
2026-05
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 23
Harness
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen to
Interfaces
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen to
Tool access
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen to
Knowledge
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen to
Grounded findings
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 56-68,166
Automatic review trigger
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, line 23
Lead scout
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 42–44
Deep reviewers
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 42–46
Disprove-it pass
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 58–68
Optional fixer
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 131–144
Evaluation loop
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 170–180
Key observation
10,000+ PRs reviewed in a typical week across 56 repositories as of May 2026
Duplicate representation of Headline claim. Same review count, repository population, period, and qualification as the headline observation.
Statement type
Metric
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
Reported by
DoorDash
Scope
Typical weekly PR reviews across 56 onboarded repositories as reported in May 2026
Denominator
Not reported
Method
Not reported
Observation date
2026-05
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 23
Key observation
Statement type
Metric
Provenance
Reported
Confidence
High
Confidence reason
The August 2026 Flux post states the weekly figure directly; the count covers review runs, which this record keeps explicit so it cannot be read as distinct pull requests.
Reported by
DoorDash
Scope
Automated code reviews per week across DoorDash agent workflows; the count unit is review runs, not distinct pull requests
Denominator
Not reported
Method
Not reported
Observation date
2026-08
- SupportsDelegating Engineering Work To Cloud-Based Agents (Flux)Preserved content.md, lines 10
Key observation
Statement type
Metric
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this 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
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 27
Key observation
Statement type
Metric
Provenance
Reported
Confidence
Medium
Confidence reason
DoorDash reports its current average review cost without an independent audit.
Reported by
DoorDash
Scope
Average cost of the current staged review workflow
Denominator
Not reported
Method
Not reported
Observation date
Not reported
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 29, 129
Key observation
Statement type
Metric
Provenance
Reported
Confidence
Medium
Confidence reason
DoorDash reports average posting latency without a distribution or independent audit.
Reported by
DoorDash
Scope
Time from pull-request opening to a posted finding
Denominator
Not reported
Method
Not reported
Observation date
Not reported
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, line 30
Lesson
Statement type
Opinion
Provenance
Reported
Confidence
Medium
Confidence reason
The precision-over-recall section states the tradeoff; the later comment-quality discussion requires a concrete file, line, and action point.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 56-68,166
Lesson
Statement type
Inference
Provenance
Catalog judgment
Confidence
Medium
Confidence reason
The report ties acceptance to its precision-first policy. A denominator of published comments does not measure recall over all defects.
- SupportsHow DoorDash built an AI code reviewer engineers actually listen toPreserved content.md, lines 56-68
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
- SupportsHow DoorDash built an AI code reviewer engineers actually listen to

How it works

Where people stay involved

Implementation details

Mechanisms

Validation and failure handling

Reported observations

Lessons

Related reading

Related implementations

Sources and research details