AI Code Reviewer

Cloudflare’s reviewer analyzes merge requests and applies approval decisions, including merge blocks for serious findings.

Company: Cloudflare
Approach type: Agent
Work: Code review
Human involvement: Unknown
Invocation: Event-driven
Interfaces: Ci, Cli
Deployment stage: Deployed
Evidence strength: Detailed primary
Entry reviewed: 2026-09-17

Copy as MD Ask ChatGPT

How it works

merge request → structured review and approval state.

Dispatch specialists

An OpenCode coordinator assigns reviewers according to change risk.^{[Source 1]}

Apply review

It approves, revokes bot approval or requests changes; a human can force approval with a break-glass comment.^{[Source 1]}

Where people stay involved

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

Not reported: The source does not establish the required human review boundary.

Supervision level
Not classified
- merge request → structured review and approval state^{[Source 1]}

How supervision levels work

Implementation details

Model

Not reported

Harness

Plugin-based OpenCode integration in CI.^{[Source 1]}

Sandbox

Not reported

Tool access

GitLab review APIs and code-reading tools.^{[Source 1]}

Knowledge

Not reported

Context management

Shared MR context and per-file patches.^{[Source 1]}

Credentials

Not reported

Interfaces

^{[Source 1]}

Validation and failure handling

Check findings

The coordinator deduplicates findings and uses code-reading tools to assess uncertain issues.^{[Source 1]}

Reported observations

Not reported

Lessons

Cloudflare reports limitations in architectural context, cross-system impact and subtle concurrency analysis.^{[Source 1]}

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

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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.

Orchestrating AI Code Review at scalehttps://blog.cloudflare.com/ai-code-review/Engineering blog · First party · Last source verification: 2026-09-17
Preserved copy

Question coverage and scope

purpose: Reported
workflow: Reported
human involvement: Not reported: The source does not establish the required human review boundary.
implementation: Reported
validation: Reported
observations: Not 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.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Harness
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Tool access
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Context management
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Interfaces
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Dispatch specialists
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Check findings
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Apply review
Statement type
Fact
Provenance
Reported
Confidence
Not assessed
Confidence reason
No explicit confidence assessment is recorded for this claim.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Lesson
Statement type
Fact
Provenance
Reported
Confidence
Medium
Confidence reason
Reported implementation lesson; not a controlled evaluation.
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations
Operating model assessment
Statement type
Inference
Provenance
Catalog judgment
Confidence
Unverified
Confidence reason
The source describes AI Code Reviewer and its work, but does not establish whether a person must approve or inspect each successful output.
Observation date
2026
- SupportsOrchestrating AI Code Review at scaleThe architecture; The coordinator helps keep things focused; Limitations

How it works

Where people stay involved

Implementation details

Validation and failure handling

Reported observations

Lessons

Related reading

Infrastructure used

Sources and research details