Authentication gets agents in. Authorization controls what happens next.

AI agents don’t just sign in. They reason, call tools, take actions and move across systems, often using delegated identities or existing machine credentials. And one agent can invoke another. As the action chain grows, it becomes harder to know who initiated the task, which agent is acting, what identity and privileges are being used and whether the action still matches the original task.

• Incorrect attribution - agent and application logs often show which account took an action, but not the user or agent behind it. That breaks provenance and makes audit and forensics harder.
• Rogue actions - agents may have access to tools that a specific user or task should not be allowed to use. Tool access should reflect who initiated the action and the context of the request.
• Standing privileges- long-lived credentials can give agents more access than a task requires. Short-lived, task-scoped permissions reduce the risk of unintended or abusive actions.

Traditional access management was not built for AI:

Discover AI agents, the identities they use, the tools and systems they can reach and the privileges available to them. Surface shadow agents and risky access paths before they turn into runtime exposure.

Create an audit trail that connects the human or system that initiated the task, the agent that acted, the resource it accessed, the action it attempted, the policy decision and the outcome.

Native APIs auto-discover sensitive resources so no privilege goes undetected as environments scale.
P0’s agentless architecture eliminates infrastructure overhead, minimizing compute costs and accelerates time-to-ZSP.

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Kubernetes, code repositories, agentic platforms and more

Organizations are still figuring out where access control should happen across the agent action chain. Different approaches start from different control points. The question is not whether each one has value. It is how far that control extends, and whether it can govern what the agent is actually allowed to do at runtime.

• Network / API-led - focuses on connectivity, reachability and the API transactions agents make
• MCP-led - treats MCP as the control point for governing which tools agents can discover and invoke
• Data-led - starts with sensitive data, then maps exposure and the identities or agents that can reach it
• Identity-led - tracks the identity chain from the originator through the agent and focuses on what that identity should be allowed to do

P0 takes an authorization-native identity-led approach, extending control from the originating identity through the agent and tool layer into the permissions granted in the target system.

MCP-led controls are useful for governing which tools agents can discover and call. But the MCP layer is only one part of the action chain. It does not, on its own, account for the full provenance of the originator or control the privileges the agent ultimately receives in the target system. In this example, the same agent makes the same tool call against the same resource. The access decision changes because the originating identity and business context are different. Same tool call. Different context. Different decision.

Everything you need to understand agentic access, runtime authorization and Zero Standing Privilege.