AI agents read data, move money, and act on live systems. TenetGraph decides what each agent is trusted to do, enforces it on every action, and produces the proof your auditors need.
To do their job, AI agents are given broad access to your CRM, your email, your payment systems. Once they have it, harm to corporate data and systems can come from many places, inadvertent or malign. The runtime is where the control needs to live, not the prompt.
Each layer answers a different question. The newest tools even give each agent an identity and watch what it accesses. But nothing defines what the agent is allowed to do with that access. That's the gap.
We derive each agent's intended boundary from its code, prompts, and tools. An adversarial agent pushes it toward the misuse paths, goal overshoot, and privilege escalation it could reach. What we find becomes an enforceable rule that holds on every action in production.
An adversarial agent pushes each agent toward the actions that overshoot its intent, surfacing the misuse paths before production does. What we find becomes an enforceable rule that holds on every action in production.
The decision happens at the moment the agent acts, not after. Harmful actions are blocked before they happen, not flagged once they're done.
Each allow and deny is logged and exportable as evidence for the audits your team already has to pass.
of organizations have no limits on what their AI agents are allowed to do, and a third keep no usable record of what agents did. (Kiteworks, 2026)
An adversarial agent surfaces where an agent could step outside its intent before it reaches production, and the rules to keep it in bounds.
action an agent takes is checked against policy in real time, then logged as evidence you can hand to an auditor.
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