How to Stop Catching the Same AI Mistakes in Every Code Review

Recurring code-review comments often reveal unwritten team policies, especially as AI coding agents generate large volumes of code without retaining prior review feedback. The proposed approach is to mine pull-request history for repeated feedback, retain rules that apply consistently across authors, and enforce each at the least costly appropriate layer: formatters and linters for mechanical issues, type checkers for compiler-detectable problems, semantic invariants for cross-cutting concerns such as authentication order or repository-based data access, and human reviewers for design judgment. Aviator Verify is presented as a tool that can analyze PR comments or documentation to draft invariant rules, which should be written as specific, standalone, verifiable statements with clear scope and exceptions where needed. Teams are advised to test rules in draft mode, promote effective ones to automated checks, and periodically prune them, turning accumulated review knowledge into repeatable safeguards rather than relying on prompts, documentation, or reviewer memory.

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