I made two AIs review each other's code for 30 days. A human still caught the bug in 5 minutes.

A developer recently experimented with using two AI agents to manage code quality: an 'author' agent to write features and a 'skeptic' agent to perform adversarial reviews. Over 30 days, the skeptic successfully identified 38 out of 41 issues, including architecture drift and race conditions. However, the system failed to catch critical logic errors, such as a flawed webhook handler that acknowledged events before database persistence. The author concludes that while AI-driven peer review is effective for routine tasks, it suffers from shared blind spots when both agents belong to the same model family. The experiment highlights that human oversight remains essential, particularly for engineers with 'scar tissue'—the experiential knowledge of past failures that AI cannot replicate. The author suggests that true independence in code review requires using different model families, adversarial prompting, and final human verification to ensure system reliability.