The bug-finding pipeline uses an LLM judge to score and prioritize files for analysis, then deploys an agentic loop where an AI agent explores code, generates HTML test cases, and runs them through existing fuzzing tools to detect memory safety issues. A verifier subagent eliminates false positives, and a patching agent generates and verifies fixes. Brian stresses the importance of clear pass/fail signals, such as Address Sanitizer, and the goal-loop pattern that allows the AI to retry far beyond human patience. He credits the success equally to the model and the harness, noting that human engineers still review every fix because AI struggles with systematic fixes across large codebases. The approach can be extended beyond security to areas like performance optimization and user experience.