AI agent governance is the set of controls that keeps agents accountable once they’re in production: who owns them, what they can access, who approves their actions, and a record of everything they’ve done. StackAI builds these controls into how agents are made and run, so every agent is governed from day one.

No. Answers are permission-scoped, so an agent only responds from documents the signed-in user is already cleared to access. Your existing system permissions carry through automatically, and role-based access controls set what each user can build, edit and use.

You can add human approval to any key decision, so reviewers can approve, reject or give feedback before the agent acts. Low-confidence outputs can also be flagged for review, keeping people in charge of high-stakes work without slowing down routine automation.

Yes. Every tool call and prompt is traceable, answers include citations back to their sources, and write-backs to your systems are tied to where the data came from. That gives auditors and reviewers a clear record of what an agent did, why, and on whose authority.

Your data is never used to train models for other customers. Each customer’s data is fully isolated, and StackAI can be deployed in our cloud, your own cloud, or fully on-premise, including on self-hosted open-source models, so sensitive data never has to leave your environment.