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Choose an identity and access management (IAM) platform that can give each AI agent a distinct identity, control what that identity may do, connect its actions to the person or system that authorized them, and support revocation and investigation. Evaluate those capabilities against your actual agents and systems—not an “AI agent” label or a vendor ranking. An interactive agent acting for a signed-in user needs a different authorization model from an autonomous agent acting under its own identity.
What should an IAM platform do for AI agents?
An agent can use tools, applications, and data, sometimes with limited human supervision. Its identity is the way systems recognize it; its authorization is the set of actions and resources it is allowed to use. A sound design keeps both visible and governed.
NIST warns that giving an agent a person’s reusable credentials creates accountability gaps. Long-lived API keys and bearer tokens can also be used by anyone who obtains them, and may grant broader access than a task requires. The practical goal is not simply to authenticate an agent: it is to establish which agent acted, whose or what authority it was using, what limits applied, and how that access can be withdrawn.
NIST’s National Cybersecurity Center of Excellence (NCCoE) describes agent IAM concerns including separate identification, authorization, delegated access, logging, and tracking data flows. Its work is evolving: the NCCoE describes a practice guide as planned work, not as a completed vendor benchmark.
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First decide how each agent acts
Start by classifying the workflows you intend to support. “AI agent” does not describe a single access pattern. Microsoft’s documentation illustrates two common patterns; these are examples from a vendor, not a universal standard or an independent product comparison.
| Pattern | How it acts | What to verify in the platform |
|---|---|---|
| Interactive, delegated agent | Acts on behalf of a signed-in user, with delegated permissions. Microsoft documents an on-behalf-of approach for this pattern. | Can the agent receive only the user authority needed for the task without being handed reusable user credentials? Can logs preserve both the agent identity and the connection to the authorizing user? |
| Autonomous agent | Authenticates under its own agent identity rather than relying on a signed-in user. Microsoft documents a client-credentials flow using agent identity for this pattern. | Can administrators bind narrowly scoped permissions to the agent, identify its owner and purpose, and revoke its access independently of a person’s account? |
Some organizations will need both patterns. Map them separately: a design suitable for a user-directed assistant may not provide the ownership, policy, or lifecycle controls needed for a scheduled or otherwise autonomous agent.
Evaluate the platform across six capabilities
1. Agent inventory and lifecycle
Ask how agents are discovered or registered, named, assigned owners, reviewed, and retired. Administrators should be able to distinguish agent principals from human users and ordinary application workloads, find agents with no accountable owner, and inspect their permissions over time. Microsoft calls uncontrolled growth without adequate visibility or lifecycle controls “agent sprawl”; use that risk as a pilot test, not as proof that a particular product solves it.
2. Runtime authentication and credential handling
Determine how an agent runtime proves its identity to the services it calls. Check whether the platform fits your workload identity approach and supports the credential issuance, protection, expiry or rotation, and revocation process you need. A static API key is not automatically equivalent to workload identity: a person who obtains the key may be able to use it, and secrets can be exposed through configuration or logs.
NIST identifies OAuth 2.0 and SPIFFE among relevant foundations and describes emerging WIMSE work as building on existing protocols. The NCCoE concept paper also discusses OIDC and SPIFFE/SPIRE. Match the approach to your runtime and operational maturity rather than selecting a protocol name in isolation.
3. Delegation and action-level authorization
Check how the platform constrains the authority an agent receives. Policies should be evaluated against the resources, actions, context, and task involved, and administrators should be able to review and withdraw privileges. For user-directed work, establish how delegated access is bounded. For autonomous work, establish which permissions attach to the agent principal and how those limits are enforced downstream.
NIST cautions against overly broad access and overreliance on human-in-the-loop approval. In practice, ask the vendor to show which actions are blocked by policy, which require an approval, and how the system behaves when approval is unavailable. Do not treat an approval prompt as a substitute for least privilege.
4. Attribution, audit, and investigation
Find out whether an investigator can connect a downstream action to the agent identity and to the person or system that authorized its access. Ask what the audit trail records about tool calls, data access, policy decisions, and denied actions, and whether records can be exported to your monitoring and audit systems. NCCoE identifies logging and transparency as areas of interest and says agent actions should be linked to the nonhuman entity.
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5. Integration and interoperability
Map the platform to the systems agents will actually use: your identity provider, cloud and runtime environment, APIs, SaaS applications, orchestration layer, and tool interfaces. Model Context Protocol (MCP) is an integration protocol for discovering and interacting with tools and data; the NCCoE concept paper says MCP relies on existing identity standards such as OAuth and OIDC for authentication and rights delegation. MCP is not, by itself, a complete IAM solution.
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The NCCoE comment summary discusses SPIFFE/SPIRE, OAuth, WIMSE, policy engines, delegation, and agent-to-agent interoperability. These efforts do not all have the same maturity. Verify implemented support and interoperability in your target architecture instead of assuming that every draft or proposal is production-ready or supported by every vendor.
6. Governance and operating fit
Compare ownership workflows, access reviews, approval paths, incident response, reporting, and policy maintenance. Establish whether the platform fits the identity operations your team already runs or creates a separate control plane that needs its own staffing and procedures. The sources available here do not establish neutral comparisons of product rollout effort or operating cost, so assess those directly with shortlisted vendors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a proof of concept around real workflows
Use a small number of representative workflows, including one delegated interaction and one autonomous task if both are in scope. Require a live demonstration in your target architecture rather than a slide-based feature confirmation.
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- Register and assign: Create an agent principal, identify its owner, and show how administrators distinguish it from a human account and a conventional workload.
- Exercise delegated access: Have a signed-in user authorize a limited task. Confirm the agent does not need the user’s reusable credentials and inspect what authority is actually granted.
- Exercise autonomous access: Have a separate agent identity perform a defined task. Confirm the permissions are constrained to the required resources and actions.
- Inspect runtime credentials: Follow how credentials are issued, protected, expired or rotated, and revoked in the agent’s runtime.
- Trace an action: Inspect the evidence connecting authorization to the agent identity and a downstream action, including a denied action where possible.
- Test governance and recovery: Find a stale or unowned agent, review its access, and demonstrate how its access is withdrawn. Confirm where relevant records are exported for investigation.
- Validate integrations: Test the identity, workload authentication, policy, logging, cloud, application, and tool systems your intended agents will use.
These are buyer evaluation questions derived from NIST and NCCoE concerns, not a NIST certification checklist. Record gaps by workflow and consequence; a capability that works for one agent pattern may not cover another.
Compare platform categories without assuming a winner
At minimum, compare your existing enterprise IAM control plane with any specialist agent-identity or authorization product under consideration. Use the same workflows and evidence requirements for both. A specialist label does not establish stronger delegation, revocation, least privilege, or auditability, and the available sources do not support naming a market-wide winner.
Microsoft Entra is one commercial example with documentation covering agent identities, interactive and autonomous patterns, and workload identity capabilities. Evaluate it against the same criteria as other shortlisted options. The consulted evidence does not establish independent testing, comparative superiority, pricing, or region-specific availability.
NIST’s agent-identity work is evolving. In an August 27, 2026 article, NIST authors Bill Fisher and Ryan Galluzzo described established IAM standards and best practices as the foundation for future agentic protocols. The NCCoE project resource hub says it received more than 600 responses to its February 2026 concept paper; that figure reflects responses to the paper, not a product evaluation or endorsement.
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