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Use AI-powered review

This guide shows you how to use AI-powered PR review.

AI-powered review dispatches one or more reviewer agents to analyze a PR. Reviewer agents produce:

  • Review nodes — structured findings with severity, file/line anchors, and suggested fixes
  • Review verdict — an aggregate assessment (ship, hold, or block)
  1. Open a PR in Control Center
  2. Click Start AI review (or use the start_ai_review MCP tool)
  3. Control Center:
    • Selects reviewer agents based on the PR’s language and domain
    • Creates a review channel for each reviewer
    • Dispatches the agents in review mode with the PR diff as context

You can also start a review from a channel by mentioning a reviewer agent.

Each reviewer produces findings as review nodes:

FieldDescription
Finding kindbug, security, performance, style, suggestion
PriorityP0 (critical) → P3 (nit)
File and lineWhere the finding is located
Original codeThe problematic code
Suggested codeThe proposed fix
ConfidenceHow confident the reviewer is
ResolvedWhether the finding has been addressed

Findings appear in the review channel as structured messages.

After analyzing all files, the reviewer produces a verdict:

VerdictMeaning
ShipNo significant issues found
HoldIssues that should be addressed before merge
BlockCritical issues that must be fixed

The verdict includes an overall confidence score and per-priority finding counts.

If multiple reviewers disagree on the same finding (e.g. one says P0, another says P3), Control Center flags the disagreement. This helps you focus attention where opinions diverge.

When you’re satisfied with the review:

  1. Click Finalize review
  2. The review summary is posted to GitHub as a PR review comment
  3. Findings are posted as inline comments (optional)

For more control, use the dispatch_reviewers MCP tool:

dispatch_reviewers(
workspace_id: "...",
pr_url: "https://github.com/owner/repo/pull/123",
reviewer_roles: ["security", "reviewer"]
)

This lets you specify exactly which roles should review the PR.