DoorDash Code Review
Suggested workflow: Filter code review comments using a process of reconnaissance, expert review, and rebuttal. Task: DoorDash implemented a multi-stage AI code review workflow involving a lead scout, two expert reviewers, and a mandatory rebuttal phase. The system processes over 10,000 pull requests weekly, filtering initial findings through expert validation and rebuttal before publishing comments. Human engineers retain override capabilities, but the automated pipeline handles the initial triage and refinement of feedback.; tools: Tool described in the source. Human role: Not stated in the source; limitations: Data is sourced from a secondary compilation of public engineering blogs, requiring verification against the original DoorDash publication for exact implementation details. The workflow assumes a high volume of PRs (10k+/week) to justify the multi-agent overhead. Integration requires specific tooling for scout, expert, and rebuttal stages, and human approval gates are necessary for final merges..
Tools
Tool described in the source
Published on Agent Work Atlas:
Overview
Suggested workflow: Filter code review comments using a process of reconnaissance, expert review, and rebuttal.
Workflow
DoorDash implemented a multi-stage AI code review workflow involving a lead scout, two expert reviewers, and a mandatory rebuttal phase. The system processes over 10,000 pull requests weekly, filtering initial findings through expert validation and rebuttal before publishing comments. Human engineers retain override capabilities, but the automated pipeline handles the initial triage and refinement of feedback.
Reported result
The workflow achieved a 60.2% conversion rate of high-severity findings into code changes, compared to 46% with their previous third-party tool. The operational cost is approximately $3 per review. These metrics are reported by the source as actual outcomes of the deployed system.
Human role
Not stated in the source
Limitations
Data is sourced from a secondary compilation of public engineering blogs, requiring verification against the original DoorDash publication for exact implementation details. The workflow assumes a high volume of PRs (10k+/week) to justify the multi-agent overhead. Integration requires specific tooling for scout, expert, and rebuttal stages, and human approval gates are necessary for final merges.
Try it
I want to pilot a multi-stage AI code review system for our repository. **Inputs:** - Repository URL: [replaceable input] - Sample PRs: [replaceable input, e.g., last 50 merged PRs] - Target: Reduce false positives and increase actionable comment rate. **Steps:** 1. **Scout Phase:** Use an LLM to scan diffs and flag suspicious areas (security, logic, style). 2. **Expert Phase:** For each flagged area, invoke two specialized reviewer agents (e.g., one for security, one for performance) to validate the finding. 3. **Rebuttal Phase:** Generate a counter-argument for each validated finding. If the rebuttal is strong, discard the comment; otherwise, keep it. 4. **Output:** Produce a JSON report of retained comments with confidence scores. **Deliverables:** - A script or workflow definition implementing the scout-expert-rebuttal loop. - A report comparing the retained comments against the original PR comments to measure noise reduction. **Safeguards:** - Run on a small sample first. - Do not auto-post comments; output to a local file for human review. - Ask clarifying questions if the repository structure or diff format is unclear.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.