Human Code Review vs. AI Code Review

AI code review uses software to scan a change automatically and flag issues; it's fast, scalable, and tireless but cannot be held accountable for its verdict and shares blind spots with the AI that wrote the code. Human code review uses a senior engineer to judge correctness, business context, and risk, and to sign off with their reputation. The first scales the obvious; the second owns the consequential.

Style and consistency issues, common bug patterns, obvious code smells, and a fast first pass at scale. It's excellent at the 80% — high volume, increasingly accurate, instant.

The exceptions and edge cases no model was trained for: a missing authorization check, business logic that's syntactically fine but wrong for the domain, architectural risk that only surfaces in production. Critically, an AI reviewing AI-generated code can't reason about context the original model was never given — and it can't be held responsible for a sign-off.

When being wrong is expensive: code touching authentication, payments, or personal data; regulated or compliance-sensitive work; architecture decisions; and anything shipping to production where you need an accountable sign-off rather than a probabilistic opinion.

Yes — that's the recommended pattern. Run automated AI review continuously for breadth and speed, then route the high-stakes changes to a human expert for an accountable sign-off. Fairy is built for exactly that second layer and integrates into AI coding workflows via MCP and a REST API so the hand-off can be programmatic.

Automation does the work that doesn't need judgment — classifying the submission, running static analysis, drafting the structural parts of the report — so the human opens the review already oriented. The expert then spends their time only on what requires judgment, and every finding and verdict carries a real person's name. The leverage is in the workflow; the accountability is always human.