Autonomous identity gives the agent its own security principal and audit trail, so it acts as itself. Delegated identity lets the agent borrow a human user’s authority through a protocol such as MCP. Hybrid identity combines both patterns, allowing the same agent to use a user’s permissions, the creator’s credentials, or its own application identity depending on the action and connector.
How the three identity models differ in practice
These models differ mainly in where authority comes from and how the agent is represented in logs, policy, and incident response. Autonomous identity treats the agent as a first-class actor. Delegated identity treats the agent as a temporary extension of a human. Hybrid identity lets the same agent shift between those modes depending on whether it is acting for itself, on behalf of a user, or under a creator-controlled connector.
The practical consequence is that the model you choose changes who owns the action, which permissions are in scope, and how much trust you place in the agent to act without a human in the loop. That also affects how teams design approvals, revocation, and auditing. For a broader view of how these patterns fit into the lifecycle of AI agents, see Agentic AI Identity Guide.
Autonomous identity is the cleanest model for accountability when the agent needs stable, non-user-specific authority and a durable audit trail. Delegated identity is better when the task is tightly tied to a person’s intent and the action should inherit that person’s access boundaries. Hybrid models are more flexible, but they also create more policy branches, more edge cases in authorization, and more ambiguity unless the organisation defines which action types can use which identity source.
When each model is the better fit
Autonomous identity fits agents that must operate repeatedly, across sessions, or across many users without inheriting a different human’s privileges every time. It is usually the right choice when the agent needs its own service lifecycle, its own revocation path, and its own monitoring. The main benefit is separation of the agent’s behaviour from any one user’s account history.
Delegated identity fits request-driven work where the agent is effectively acting for a specific person, such as searching, composing, or submitting something within that user’s rights. This model is strongest when the organisation wants the user’s permissions, consent, and accountability to stay central. It is weaker when the agent needs persistent background capability or access that should outlive a single user session.
Hybrid identity is most useful when one agent must do both kinds of work. A common pattern is to use delegated authority for user-facing actions, creator or operator credentials for maintenance or connector setup, and an autonomous application identity for internal orchestration. The trade-off is that each mode must be explicitly bounded, or the agent can become difficult to reason about during reviews and incident investigations.
For teams comparing protocol choices for delegated flows, RFC 8693: OAuth 2.0 Token Exchange is a useful reference point because it formalises token exchange patterns used in on-behalf-of designs.
What changes for security, governance, and auditability
The identity model changes the blast radius of a mistake. In an autonomous model, a misconfigured permission applies to the agent principal directly. In a delegated model, the risk inherits the user’s rights and may expand if the agent can chain actions the user never intended. In a hybrid model, the same workflow can cross trust boundaries, so the organisation must track which identity was used for each action rather than assuming one policy model covers everything.
That makes logging and policy enforcement especially important. Practitioners need to know whether an action was taken as the agent, as the user, or under a connector credential, because the response differs in each case. A useful operating model is to align the agent’s identity source with the smallest authority needed for the action, then log the decision path so reviews can distinguish user intent from autonomous execution. For implementation guidance on those controls, see AI Agent Authorisation Guide and AI Agent Observability, Audit and Incident Response Guide.
Hybrid identity also creates a governance question: who is responsible when the same agent can act under different principals. That is not a purely technical issue. It affects approval workflows, offboarding, connector review, and whether a security team can prove that privilege stayed bounded across modes. For a deeper explanation of how agent authority and audit trails change as autonomy increases, AI Agents vs Agentic AI is a useful companion.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI agent identity models directly determine how authority is granted and abused. |
| Recommendation — Apply ASI03 by constraining each agent to the least authority needed for its mode. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Service and Workstation Devices) | Autonomous and hybrid agent principals often authenticate as services or workloads. |
| AC-6 — Least Privilege | The comparison turns on how much authority the agent should receive in each mode. | |
| Recommendation — Use IA-9 to authenticate non-human agent principals and separate them from user identities. Enforce AC-6 so agent actions only inherit the minimum permissions required. | ||
| NIST CSF 2.0 | PR.AA-05 — Identity Management, Authentication and Access Control | The models differ by how identities are represented, authenticated, and authorised. |
| Recommendation — Define and enforce distinct agent identity paths for autonomous, delegated, and hybrid actions. | ||
Practitioner Guidance
What to prioritise: Decide first whether the agent needs persistent authority, user-bound authority, or both. That choice should drive the principal model before you worry about the connector or model stack.
What to verify: Verify that every action can be attributed to the correct identity source and that revocation works for each source independently. If you cannot answer “which principal acted” in an incident review, the model is too loose.
Common mistake: Do not use hybrid identity as a convenience default. It often becomes a hidden privilege aggregation layer unless the organisation defines clear rules for when the agent may switch identity sources.
Decision rule: If the action changes external state, spend money, access customer data, or touch production systems, prefer the narrowest identity that still supports the task and require stronger approval or logging for any fallback to delegated or creator authority.
Practitioner takeaway: The right model is the one that makes authority obvious after the fact, because in agent systems, the hardest security problem is often not execution, but proving under whose authority the execution happened.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on November 5, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org