AI agent identity is still split between human and non-human models

At a glance

What this is: Aembit argues that AI agent identity cannot be handled cleanly as either human IAM or NHI because agents may switch between delegated authority and autonomous access.

Why it matters: IAM teams need a policy model that can classify agent behaviour by action context, because a single identity posture may not fit all AI agent tasks.

Context

AI agent identity is a governance problem before it is a tooling problem. The article argues that AI agents do not fit neatly into existing human or non-human identity models because their access can shift between delegated authority and autonomous execution. That makes identity classification, scope, and auditability the first-order issues for IAM teams.

Traditional IAM assumptions break down when the same software entity can behave like a user in one workflow and like a workload in another. The article treats AI agents as dynamic actors whose permissions may need to change by task, which means identity management has to account for context, lifecycle boundaries, and accountability as part of design.

Key questions

Q: How should IAM teams classify AI agents that switch between delegated and autonomous work?

A: Use the agent’s current operating mode, not a permanent label, as the basis for classification. If the agent is acting on behalf of a person, govern it like delegated human access. If it is making independent runtime decisions with its own credentials, govern it like a non-human identity. The key is to classify the action context, not just the model or interface.

Q: Why do AI agents create new risk in non-human identity management?

A: AI agents create risk because they operate as software identities with delegated authority, but many organisations do not track them with the same discipline applied to users or service accounts. They can connect quickly, persist across teams, and accumulate permissions that are hard to review. That combination increases the chance of unnoticed access drift and credential exposure.

Q: What are the signs that AI agent permissions are too broad in enterprise environments?

A: Common warning signs include agents accessing tools they do not need, performing irreversible actions without confirmation, retrieving cross-tenant or unrelated data, and acting with long-lived credentials. If logs show the agent is using broad user permissions instead of scoped access, the control model is too loose and the environment is exposed to accidental or malicious misuse.

Q: What should organisations do when an AI agent is no longer needed?

A: They should revoke the agent’s access, retire its credentials and confirm that its downstream integrations are disabled before the business use case is closed. If the identity stays active after the task ends, the organisation has created lingering authority with no live business owner behind it.

Technical breakdown

Why AI agent identity breaks human IAM assumptions

Human IAM models assume stable users, predictable sessions, and access that can be reviewed after the fact. AI agents do not behave that way. They may act across multiple domains, choose actions at runtime, and vary their access needs from one task to the next. That makes simple user-style provisioning too blunt, especially when the agent can use delegated authority on behalf of a person and then continue operating independently. The technical issue is not only authentication. It is how identity systems encode context, scope, and traceability when the actor is not fixed to a single mode of operation.

Practical implication: classify agent access by task context, not by a one-time identity label.

Why NHI controls still do not fully solve agentic access

Non-human identity controls fit workloads that have defined capabilities, limited workflows, and relatively stable permission sets, even when those workloads are ephemeral. AI agents differ because their actions are not fully deterministic. They can invoke tools across domains, alter their sequence of operations based on reasoning, and shift between sessions in ways that make static permission design fragile. In other words, the agent may look like an NHI, but its runtime behaviour is more variable than a script or service account. That creates a gap between workload-style access governance and the way agents actually execute.

Practical implication: do not assume workload-style provisioning alone is enough for agent runtime behaviour.

Delegated authority and autonomous execution create a mixed identity pattern

The article’s most important technical point is that AI agents can combine human-like delegation and non-human operational access inside one actor. That creates a mixed identity pattern, where one action may require user-scoped authority and another may require machine-scoped credentials. Technically, this is a lifecycle and policy problem as much as an access problem. The system has to know when the agent is operating on behalf of a person, when it is acting on its own, and how to preserve audit continuity across both states. Without that distinction, logging and enforcement become ambiguous.

Practical implication: design policy and logging to preserve mode-switch visibility across the full agent lifecycle.

NHI Mgmt Group analysis

AI agent identity should be treated as a mode-switching governance problem, not a single identity class. The article shows that agents can move between delegated human authority and autonomous non-human action inside the same workflow. That means the old binary split between workforce identity and workload identity is too coarse for agentic systems. Practitioners need to think in terms of identity state, not just identity type.

Least privilege for AI agents is action-scoped, not role-scoped. The article’s strongest implication is that permissions cannot be safely assumed at provisioning time if the actor can decide what to do at runtime. That breaks the comfort of static roles and broad user-style delegation. The practitioner takeaway is that access boundaries must be evaluated at the moment of action, not only at onboarding.

Auditability becomes weaker when an agent can look human in one step and machine-like in the next. The article points out that AI agents may operate through human-centric and NHI-centric pathways, which can blur incident response and compliance review. That is not just an observability issue. It is an accountability issue because the same event may be interpreted under two different governance models. Teams should treat trace continuity as a design requirement.

Agentic identity will force IAM programmes to reconcile authentication, authorisation, and lifecycle in one policy model. The article does not describe a future where IAM is replaced. It describes a future where IAM must decide, per action, which identity model applies and how far authority extends. That makes the governance challenge broader than access control alone. Practitioners should expect agent identity to reshape classification, certification, and offboarding logic.

Mixed identity is the right concept when a single AI agent depends on both human delegation and machine autonomy. The article’s central contribution is to reject the idea that agents are simply humans-with-APIs or workloads-with-LLMs. The more accurate model is conditional identity selection based on context and task. That reframes the programme question from 'what is the agent' to 'which identity posture governs this action'.

What this signals

Mixed identity is becoming the practical norm for agentic systems. As AI agents move between user-delegated and autonomous activity, programmes that rely on a single identity classification will struggle to enforce consistent governance. The more useful model is to decide identity posture per action, then preserve that decision in policy and logging.

Agent governance will converge on context-aware access decisions. The article points toward a future where IAM is not only about authenticating an actor but about interpreting what the actor is doing right now. That shifts programme design toward runtime classification, tighter scope boundaries, and lifecycle rules that can follow the agent across modes.

For practitioners

• Classify agent access by operating mode Map each AI agent to the tasks it performs, then decide when it is acting on behalf of a person and when it is operating autonomously. Keep those modes separate in policy so the same actor is not forced into one permanent identity posture.
• Scope credentials to the specific action Grant the narrowest authority needed for the current task, rather than assigning a broad standing role to the agent. Re-evaluate scope when the agent changes context, data domain, or execution path.
• Preserve audit continuity across identity modes Log delegated and autonomous actions in a way that lets investigators reconstruct which identity posture governed each step. Keep the mode transition visible so review teams can distinguish user-representative behaviour from machine execution.
• Build lifecycle rules for agent offboarding Define how agent access is revoked when the workflow ends, the model changes, or the agent is retired. Treat agent offboarding as a governance event, not just a credential cleanup task.

Key takeaways

• AI agent identity cannot be reduced to either human IAM or NHI because the same actor may switch between delegated authority and autonomous execution.
• The biggest governance risk is not whether the agent can authenticate, but whether its access scope and audit trail still make sense as it changes modes.
• IAM teams should classify agent behaviour by task context, preserve trace continuity across identity states, and define explicit offboarding rules.

Key terms

• Agentic Identity: An agentic identity is a non-human identity used by an autonomous system that can act, call tools, and access data with execution authority. It needs the same governance discipline as other privileged identities, plus runtime context, ownership mapping, and revocation paths.
• Delegated Authority Model: A delegated authority model defines who is allowed to approve, review, or execute control-related decisions across the enterprise. It helps ensure requests reach the correct responsible party, especially when control owners, managers, and process owners sit in different teams, regions, or systems.
• Identity posture: Identity posture is the measurable state of who and what can access systems, data, and services at a given point in time. It covers human users, service accounts, applications, and external parties. Strong identity posture depends on visibility, entitlement accuracy, review cadence, and dependable offboarding.
• Mode Switching: The movement of an AI agent between human-like and non-human-like behaviour within the same workflow. This matters because different modes can require different permissions, different audit treatment, and different lifecycle actions, even when the underlying software is the same.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.