AI Review Mode (Built-in Reviewer)

Use an AI reviewer agent to gate the coder: concrete reject criteria and a tight loop.

Agent DevelopmentAdvanced

Why

MCP's reference server repo has ~90,000 GitHub stars, and the Agentic AI Foundation grew from 49 founding members (December 2025) to 247 by August 2026, signaling rapid multi-agent ecosystem growth. A built-in reviewer catches mechanical issues (missing tests, scope creep, banned patterns) before a human looks at the diff.

This scales review with AI generation and gives the coder concrete feedback to iterate against.

When

Use review mode when PR volume exceeds human capacity or when you want a self-correcting coder loop. Keep humans for architecture and security.

How

1. Define concrete reject criteria (no 'any', every new fn has a test, in-scope only). 2. Run the reviewer on the coder's diff; it outputs APPROVE or REJECT + reasons. 3. On REJECT, feed reasons back to the coder; cap at 3 iterations. 4. On APPROVE, run the full gate and human review. 5. Track AI vs human verdict agreement to tune criteria.

Example

Reviewer prompt: "Reject if: any 'any' type, new fn without test, file outside src/api/. Output APPROVE or REJECT + reasons." Coder diff has 'any' in 2 files → REJECT "any in users.ts:14, books.ts:22". Coder fixes → APPROVE → human reviews architecture only.

Common Problems

⚠Error: Agent loops infinitely on the same tool call → add a max-iterations guard and a distinctness check on the last N actions.
⚠Error: Cannot find module '@modelcontextprotocol/sdk' → `npm install @modelcontextprotocol/sdk`; agents need the SDK to call MCP tools.
⚠Error: "Tool timed out after 30000ms" → increase the timeout config or break the task into smaller sub-tasks the agent can complete incrementally.

Best Practices

✓Give the reviewer checkable criteria, not 'is this good'.
✓Log AI verdict vs final human verdict to prune noisy rules.
✓Measure the outcome with a concrete metric (time saved, defect rate, test coverage) so the practice can be justified and improved.

FAQ

What is AI Review Mode (Built-in Reviewer)?

Use an AI reviewer agent to gate the coder: concrete reject criteria and a tight loop. It helps developers quickly grasp the core value and use cases of AI Review Mode (Built-in Reviewer).

How do I get started with AI Review Mode (Built-in Reviewer)?

To get started with AI Review Mode (Built-in Reviewer), focus on these points first: Define concrete reject criteria (no 'any', every new fn has a test, in-scope only).

What are common mistakes with AI Review Mode (Built-in Reviewer)?

Common mistakes when using AI Review Mode (Built-in Reviewer) include: Error: Agent loops infinitely on the same tool call → add a max-iterations guard and a distinctness check on the last N actions.; Error: Cannot find module '@modelcontextprotocol/sdk' → `npm install @modelcontextprotocol/sdk`; agents need the SDK to call MCP tools.; Error: "Tool timed out after 30000ms" → increase the timeout config or break the task into smaller sub-tasks the agent can complete incrementally..

What are the best practices for AI Review Mode (Built-in Reviewer)?

Best practices for AI Review Mode (Built-in Reviewer) include: Give the reviewer checkable criteria, not 'is this good'.; Log AI verdict vs final human verdict to prune noisy rules.; Measure the outcome with a concrete metric (time saved, defect rate, test coverage) so the practice can be justified and improved..

How do I learn more about AI Review Mode (Built-in Reviewer)?

Start with the official docs, then search real-world examples on GitHub. Validate patterns in a small scratch repo before applying them to production code.