AI agent management builds on traditional identity and access management (IAM), but adds controls for what an autonomous software actor can do through tools and other agents, whose authority it uses, and how those actions are traced and stopped. The goal is not to replace IAM: it is to apply identity, authorization, governance, and audit controls to agents’ ability to act across systems.
What is different about managing an AI agent?
Traditional IAM answers questions such as which person or application is authenticating and what resource it may access. Agent management must also account for how software makes decisions and takes actions: which tools it can invoke, what data and APIs those tools expose, whether it is acting for a user or independently, and whether it can delegate work to another agent.
That makes an agent more than a login to provision. It needs an identifiable principal, a defined purpose and accountable people, permissions matched to its operating context, and an audit trail that connects actions to the agent and the authority behind them. NIST says traditional IAM approaches “may not fully address emerging challenges” as agents take autonomous actions. Its NCCoE project is intended to apply identity standards and best practices to software agents and develop practical implementation resources. NIST NCCoE project resource hub
Should AI agents have their own identities? In general, each deployed agent should be identifiable as an actor rather than hidden behind a shared human or application credential. That identity should be linked to the people accountable for it, without confusing the agent’s identity with the user whose authority it may sometimes use.
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Traditional IAM and agent management compared
The distinction is an expansion of familiar controls, not a choice between IAM and a separate system. The comparison below describes the additional questions to resolve when the identity is an agent; it does not imply that every traditional IAM deployment lacks service identities, delegation, or audit capabilities.
| Control area | Traditional IAM concern | Additional agent-management concern |
|---|---|---|
| Identity and discovery | Identify the person or application requesting access. | Discover and distinguish agent identities; record each agent’s purpose and capabilities. Microsoft documents discovery and agent metadata in its Entra Agent ID framework. Microsoft security for AI overview |
| Accountability | Associate access with an account or application owner. | Assign an accountable owner and sponsor to the agent, and keep those relationships attached to its lifecycle. Microsoft recommends both at creation. Microsoft Agent ID best practices |
| Authorization context | Decide whether access is for an application acting independently or a user acting through an application. | Make an explicit choice between autonomous, app-only access and actions on a user’s behalf. Microsoft recommends app-only permissions when there is no user context, and on-behalf-of or delegated access when user policies and consent should apply. Microsoft Agent ID best practices |
| Permission scope and credentials | Grant access needed for an application or user to perform its job. | Limit access to the required data, models, APIs, and tools; use scoped credentials and avoid permissions broader than the chosen authorization model requires. Microsoft recommends managed identities, federated credentials, or certificates in production, and isolating credentials across unrelated environments. Microsoft identity and least-privilege guidance |
| Tools and delegation | Control access to applications and resources. | Treat each tool call and agent-to-agent handoff as an authorization boundary: establish trust explicitly, bind the call to the initiating identity, and authorize its exact action and target. Microsoft’s least-privilege guidance addresses AI identity and access; NIST includes authorization and prompt-injection mitigation among topics for its project. Microsoft identity and least-privilege guidance NIST concept-paper announcement |
| Review and revocation | Review access and remove it when it is no longer needed. | Cover the agent’s full lifecycle, including expiry, disablement, deactivation, and decommissioning; ensure revocation reaches credentials and connected access paths. Microsoft documents governance and access-review capabilities in its product overview. Microsoft security for AI overview |
| Auditability | Record authentication and access events for investigation and accountability. | Preserve enough context to trace actions to the agent, the initiating user when applicable, and the authority used. NIST identifies auditing and non-repudiation as issues for its project; Microsoft says its framework supports agent activity logging. NIST concept-paper announcement Microsoft security for AI overview |
| Policy consistency | Apply access policy to identities and their resources. | Where a platform uses agent blueprints or templates, determine how policy changes affect both existing and future instances. Microsoft describes blueprint-level controls; its best practices recommend applying policies at that level. Microsoft security for AI overview Microsoft Agent ID best practices |
How to manage identity and access for AI agents
A practical program starts with the agent’s operating model and follows it through retirement. These steps translate established IAM principles into decisions about autonomous action and delegation.
- Inventory agents and assign accountability. Identify agents in use, including those created by teams or connected through platforms. For each, document its purpose, capabilities, owner, and sponsor. Maintain that record through deactivation and decommissioning.
- Choose the authority model before granting access. If an agent acts independently without a user context, use an app-only model with the required application permissions. If it performs work on a user’s behalf, use a delegated or on-behalf-of flow so applicable user policies and consent govern access. These are Microsoft implementation recommendations, not a universal standard. Microsoft Agent ID best practices
- Limit permissions to the task. Grant only the data, APIs, models, and tools needed. Prefer scoped, short-lived credentials where supported, and review permissions for creep as the agent’s purpose changes. Do not grant broad application permissions when delegated access is sufficient. Microsoft identity and least-privilege guidance
- Secure every tool call and handoff. Define which agents may trust or invoke one another. Authorize the specific action and target, bind the request to the identity that initiated it, and require fresh approval for irreversible or high-impact actions. An agent’s ability to call a tool should not itself be treated as permission for every action that tool can perform.
- Set lifecycle controls and preserve evidence. Review access, set expiration where appropriate, and ensure administrators can disable the agent and revoke its credentials and downstream access. Log agent activity with enough context to investigate who or what initiated an action and under which authority. Microsoft’s overview describes agent activity logging and governance capabilities; NIST identifies auditing and non-repudiation as areas the project is considering. Microsoft security for AI overview NIST concept-paper announcement
What Microsoft Entra documents today
Microsoft describes its Entra Agent ID framework as supporting agent identity blueprints and instances, metadata, discovery, activity logging, Conditional Access, identity-risk signals, governance, access reviews, and time-bound access packages. These are Microsoft’s product capability claims, not a neutral industry standard or a guarantee that every feature is generally available. Microsoft’s identity-governance overview marks agent identity governance as preview. Microsoft security for AI overview Microsoft identity governance overview
Microsoft’s implementation guidance also recommends assigning an owner and sponsor, documenting purpose and scope, applying policy at blueprint level, using managed identities, federated credentials, or certificates in production, and isolating credentials across unrelated environments. Those are vendor recommendations; organizations should map them to their own systems and risk requirements. Microsoft Agent ID best practices
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhat NIST’s work does—and does not—establish
In its February 5, 2026 announcement, NIST said the NCCoE was seeking feedback on a potential project to apply identity standards and best practices to software agents. The announcement names identification, authorization, auditing, non-repudiation, and prompt-injection mitigation as discussion topics. The current project hub describes an effort to produce practical implementation resources, with an intended SP 1800-series practice guide containing example implementations, architectures, build details, and lessons from laboratory work using commercially available technologies. That intended guide should not be mistaken for a completed publication. NIST concept-paper announcement NIST NCCoE project resource hub
The hub reports over 600 responses to the concept paper, attributed to NIST NCCoE in 2026. That is a response count, not a measure of agent adoption, security incidents, control effectiveness, or consensus. The cited sources do not establish a suitable statistic for any of those claims.
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