I built a deterministic execution-governance layer for AI agents. Where does it break?

A developer released Execution Governance AI (EGA) V9, an open-source runtime framework that adds a deterministic execution-governance layer for AI agents, reporting 100% attack detection with 0% false positives and 0% false negatives in its evaluation. The framework verifies workflows, evaluates trust state, and triggers fail-closed containment when governance conditions are violated, and the developer is inviting adversarial testing to identify weaknesses.

Autonomous AI agents can increasingly call tools, invoke APIs, and take actions in external systems. That raises an implementation question: Can execution itself be governed deterministically before an agent’s action is allowed to proceed? I built an open-source runtime framework to investigate that question: Execution Governance AI EGA V9 . Rather than replacing the LLM or agent framework, EGA V9 introduces a separate execution-governance layer for verifying workflows, evaluating trust state, and triggering fail-closed containment when predefined governance conditions are violated. The implementation and reproducible evidence are public: GitHub: Paper / methodology: Within the evaluated threat model and experimental conditions, the reported evaluation detected 100% of the injected attacks, with 0% false positives and 0% false negatives . Those numbers are not a claim that every autonomous-agent attack can be stopped. They describe the reported evaluation within its defined scope. What I am more interested in now is the implementation itself: If you were trying to break this execution-governance boundary, what would you test first? Reproduction attempts, adversarial cases, implementation criticism, and failures are all useful. What am I missing?