review

To review code changes before merging to production, you need an AI-driven assessment that verifies plan compliance, security vulnerabilities, performance, integration safety, and test coverage using workflow artifacts and git diff data.

AI code review for production readiness is an expert-level assessment that checks post-implementation code against the original plan, scanning for security vulnerabilities, resource leaks, and integration contract violations before merging.

To verify that new code fulfills acceptance criteria, you perform plan compliance verification by evaluating the git diff against workflow artifacts to ensure every defined task and acceptance criterion is safely implemented.

Yes, AI code review works with git diff and test execution data, requiring access to workflow artifacts and test results to validate code correctness, integration safety, and rollback readiness before production deployment.

The best way to assess integration safety and security vulnerabilities is to analyze the code implementation for resource leaks, inefficiencies, and external service contract violations while validating that all new paths are tested and passing.

Your code review needs access to workflow artifacts because it requires the original plan and execution context to verify that post-implementation code changes correctly fulfill acceptance criteria and are safe for production.