How Adversarial Self-Review Prompts Make AI Coding Agents Catch Their Own Bugs

AI coding agents are effective at building working implementations but tend to overlook subtle flaws once a solution is in place, much like human developers who become blind to their own code's weaknesses. A practical technique gaining traction among developers involves prompting the agent to switch roles after completing a task — instructing it to act as a hostile reviewer who assumes the code is broken and must find evidence of that. This adversarial framing shifts the model's focus from validating the happy path to probing for edge cases, race conditions, security vulnerabilities, incorrect error handling, and flawed tests. Developers are advised to separate the review step from the fix step, so the agent's reasoning remains transparent and findings can be evaluated before any changes are made. For larger changes, assigning two distinct personas — one as the implementer and another as a senior reviewer who did not write the code — can further sharpen the quality of the critique.