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?