AI Code Review

When code generation gets faster, review has to get stricter. Human review alone does not scale well once PRs get large and frequent, and it is easy to slip into rubber-stamping. AI review can help — but only if it is built as an independent, adversarial system.

What I’ve found works in practice:

• Separate code generation from review. The agent that writes code should not be the one approving it.
• Run review locally before the PR. Fix P1/P2 issues early so humans focus on architecture, not noise.
• Feed the review engine real context: repo standards, ticket acceptance criteria, and team conventions.
• Rank findings by severity. Security and data integrity issues should block merges; minor issues should not.
• Codify recurring patterns. If the same bug appears twice, turn it into a reusable rule or skill.

The hard part is not the model. It is the system around it: retrieval, evidence, triage, monitoring, and trust.

How are you handling AI review in your workflow today?