AI code review is now standard in engineering teams. Done well, it surfaces common issues and lets senior engineers focus on architecture and judgment. Done badly, it produces noise that engineers learn to ignore. Here is the practical workflow.
Security anti-patterns. SQL injection vectors, exposed secrets, unsafe deserialization.
Common bugs. Null pointer paths, race conditions, off-by-one errors.
Style consistency. Naming conventions, structural patterns.
Documentation gaps. Public functions without docstrings.
Test coverage gaps. Untested edge cases.
Architecture decisions. Is this the right pattern for our system? Senior judgment.
Business logic correctness. AI does not know what the code SHOULD do.
Performance implications at scale. AI catches obvious issues; subtle scaling problems require understanding the system.
Team conventions not in style guides. Unwritten norms.
1. AI reviews FIRST. Catches obvious issues before human time is spent.
2. Engineer addresses AI feedback before requesting human review. Filter the noise.
3. Human reviewer focuses on architecture, judgment, and business logic. Where their time adds the most value.
4. AI review tools should be tuned — not all signals matter equally. Disable rules that produce false positives.
5. The reviewer always has the final say. AI is advisory, not authoritative.