Topic starter
11/11/2026 6:02 pm
TL;DR: Delegated actions, runtime context and multi-step tool use make a single access decision insufficient for control, according to IndyKite research. Practitioners need policy enforcement to follow the transaction across agents, tools and data sources.
Editorial analysis by NHI Mgmt Group, based on content published by IndyKite: “Extending zero trust for agentic AI”.
Key questions
Q: Why do delegated agent actions create accountability problems?
A: Delegated agent actions can hide which model chose the path, which policy allowed it, and which tool executed it.
Q: Why does runtime intent matter for agent authorisation?
A: Runtime intent matters because agents can pivot, chain tools, and continue acting after the original request has shifted in scope.
Q: How do agentic AI controls differ from traditional Zero Trust practices?
A: Traditional Zero Trust often stops at verifying identity and device posture, but agentic controls must verify what the actor does after access is granted.
Practitioner guidance
- Map every agent handoff Inventory where users delegate to agents and where agents delegate further to tools or other agents, then require a policy check at each transfer of authority.
- Enforce runtime authorization Move control decisions from session start to action time so each tool call is judged against the current context, not the initial login state.
- Preserve action attribution Record both the active executor and the original requester for every meaningful agent action so reviewers can trace delegated work end to end.
Bottom line: Agentic AI changes the security unit of analysis from a login to a live workflow with multiple delegated actions.
Glossary terms: Agentic AI, Delegated authority, Runtime Enforcement, Zero Trust
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11/11/2026 6:07 pm
Zero trust for agentic AI is a control model for action chains, not just identities: once a system can retrieve, decide, call tools and hand work to other agents, the security problem shifts from who logged in to which actions remain valid at each step. That makes continuous policy evaluation the real governance boundary. Practitioners should measure control coverage across the whole workflow, not just the first request.
A few things that frame the scale:
- The agentic AI market is already worth $5 billion and is projected to reach $50 billion by 2030.
A question worth separating out:
Q: What should teams log when AI agents perform sensitive actions?
A: Log the delegator, the delegate, the declared purpose, the scope of resources, the expiration bound, and the policy decision rationale. Those fields let security and compliance teams answer who authorised the action, why it was allowed, and whether the workflow stayed within consented intent.
👉 Read our full editorial: Agentic AI delegation needs continuous zero trust across workflows