Three-Step Audit Gate to Catch Flaws in AI-Generated Code Patches

A software auditing framework proposes a three-part gate to validate patches written by AI agents before any human review begins. The first layer uses property-based checks with random inputs to verify that core behavioral laws — such as no dropped or duplicated records — hold regardless of implementation shape. The second layer locks fixture files using SHA-256 checksums stored in a manifest, so any silent modification to test data triggers an immediate gate failure. The third layer introduces an expiring flaky-test freeze, where unstable tests are skipped only within a defined window and automatically re-enabled after a set expiry date. Together, the three steps are designed to catch structural regressions that a passing test suite would otherwise miss.