Why agents change the governance problem
Governance tooling was built for a world where a human deploys a model, you register it, and it stays put. Agentic AI does not fit that world. An agent decides which tools to call, reaches data on its own, and chains actions no one scripted. The decision that carries risk happens at runtime, inside the agent's loop, not at deployment. That single shift is why a registry that lists your agents governs almost nothing that matters: by the time an action is logged, it has already happened. An agent governance platform has to control the action, not record the agent.
The core requirement: enforce at the action
The non-negotiable capability is interception at the point of action. When an agent attempts a tool call or a data access, the platform should evaluate it against policy and allow, block, or modify it before it executes. That means each agent carries a scoped identity, least-privilege rules are checked on every call, personal data is redacted before it reaches an external model, and requests route only to approved providers. A platform that surfaces what an agent did after the fact provides visibility; a platform that decides whether the action may proceed provides governance.
Identity and least privilege for agents
Agents multiply fast, and each one needs boundaries. Treat every agent as its own principal with a scoped identity, not as an extension of the user who launched it. Grant the narrowest set of tools and data each agent needs, and verify that grant on every request rather than trusting an agent because a prior call succeeded. This zero-trust posture is what keeps one over-permissioned agent from becoming a path to systems it was never meant to reach. Ask any platform how it scopes and verifies agent identity per action.
Evidence that survives an audit
Agentic systems make audit harder because the chain of actions is generated on the fly. The platform should record each action as it is evaluated: which agent, which tool or data, what policy applied, what was redacted or blocked, and the outcome, mapped to the obligations you answer to. When this record is a by-product of enforcement, you can reconstruct exactly what an agent did and why a step was stopped. When it is a separate logging effort, the trail has gaps in precisely the autonomous moments that matter most.
How to run the evaluation
Do not buy from a feature matrix. Stand up a test agent, give it a task that should trip a boundary, and watch what the platform does. Does it block the out-of-policy tool call before execution, or note it afterward? Is identity scoped per agent or assumed at the user level? Does sensitive data get redacted before it leaves? Is the audit record generated automatically and mapped to a named obligation? The platforms that pass on a live agent are the ones built for how agents actually behave.
Frequently asked questions
Why can't a model registry govern AI agents?
A registry records that an agent exists. Agents make consequential decisions at runtime by calling tools and chaining actions, so control has to be enforced on each action, which a static catalogue cannot do.
What does zero trust mean for AI agents?
Each agent is treated as its own principal with a scoped identity and least-privilege access that is verified on every request, so no agent is implicitly trusted to reach a system because an earlier call worked.