Green CI Isn't Proof: Continuous Verification for AI Code

Continuous verification is presented as a pre-merge practice for proving that a specific code change fulfills its intended user-facing behavior by exercising a running application, rather than merely confirming that code compiles or existing tests pass. The text argues that AI-generated code increasingly passes syntax checks while retaining substantial security and logic risks, citing research showing security pass rates near 55% despite syntax pass rates above 95%, alongside increases in deeper issues such as privilege escalation and architectural flaws. It distinguishes continuous integration, continuous testing, and continuous verification, positioning verification as an independent, behavior-based layer derived from tickets or acceptance criteria instead of implementation details, so that agents cannot reproduce a bug in both code and its tests. Recommended implementation includes a quick check in the agent loop, targeted browser-based verification as a required pull-request gate, broader tests after merge, and critical production monitoring, with artifacts such as screenshots, videos, console errors, network failures, and explicit exit codes supporting reviewer confidence. The text promotes TestMu AI’s Kane CLI as a tool for running natural-language browser objectives in CI and suggests measuring effectiveness through escaped defects in AI-authored changes, coverage of changed user flows, time to first failure, and verification flakiness.

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