Use AI to reduce code review time by preparing a smaller, better-evidenced change before a human opens the diff. The agent should explain intent, identify risky areas, run deterministic checks, and point reviewers to decisions that require judgment. It should not replace merge accountability.
Before review, ask AI to compare the implementation with the requirement and list changed behavior, interfaces, data, permissions, and dependencies. Have it run formatting, linting, types, tests, and build checks, then report exact results and failures. Request a file-by-file summary that highlights generated code, migrations, security-sensitive paths, and missing coverage. Split unrelated edits into separate changes and remove formatting churn. A second AI pass can look for regressions or inconsistent callers, but findings should cite concrete locations and failure scenarios.
Reviewers can then work by risk: authorization, money, data loss, concurrency, public contracts, and rollback first; routine mechanical changes later. Track review minutes, correction rounds, escaped defects, and false-positive comments to see whether the process is actually improving.
The fastest review is an understandable change. Keep the requirement, plan, evidence, and diff aligned, and require authors to resolve uncertainty before requesting approval. AI earns time back when it reduces navigation and reconstruction, leaving humans to decide whether the behavior is right.
