To verify an AI agent acting for a person or system, verify both identities and preserve their relationship: authenticate the agent workload, make authorization depend on the delegator and the permitted scope, and record enough evidence to reconstruct what happened. A user login or API credential by itself does not establish which agent acted, whether it had valid authority for that action, or whether the authority had since changed.
Why an agent needs an identity of its own
An AI agent is not simply a human user with a different interface. Current IETF guidance describes an agent as a workload that interacts iteratively with a large language model and external tools, services, or resources. Each service it contacts needs a way to identify and authenticate that workload.
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When the agent acts for a person or another system, there are two linked principals: the delegator that grants authority and the agent that exercises it. The agent should remain identifiable in its own right, while the delegator’s identity and relevant context inform the authorization decision and remain available in the audit trail. Otherwise, a record may show only a human account even though an autonomous workload made the actual tool call.
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The September 15, 2026 IETF Internet-Draft on AI Identity Management Systems (AIMS) captures the goal this way: “An Agent Identity Management System ensures that the right Agent has access to the right resources and tools at the right time for the right reason.” That draft is work in progress, not a final standard.
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Authentication and authorization answer different questions
Authentication: which workload is connecting?
Authentication establishes the identity of the agent workload to the service it is contacting. That requires an identifier and credentials associated with it. Teams need to decide how the identifier is assigned and maintained, how credentials are provisioned and bound to it, and how credentials are rotated or revoked.
A credential proves an identity only within the system and conditions that issued and validate it. It does not, by itself, show that the agent is allowed to perform every operation available to that identity.
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Authorization: may it perform this operation?
Authorization evaluates a particular request: whether this agent may access a model, tool, service, or resource for the proposed action. The decision can account for the agent’s identity as well as the delegator, purpose, scope, and context. A permission that is appropriate for one task or set of available tools may be too broad for another.
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This distinction matters especially in “on behalf of” scenarios. The agent should not become an undifferentiated proxy for the human. A useful authorization record retains both who granted the authority and which agent exercised it.
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What a practical identity architecture must decide
Agent identity management is not one login screen or protocol. The September 2026 AIMS draft presents it as a conceptual model that can be distributed among identity providers, provisioning services, authorization servers, policy engines, and runtime enforcement points. It proposes composing existing standards, including WIMSE and OAuth-family specifications; it does not prescribe one product or deployment layout.
- Identity: Decide how each agent is identified, which attributes are needed, and whether its identifier remains stable across restarts, deployments, or changes in purpose.
- Credential lifecycle: Define how credentials are issued and bound to the agent, then rotated, suspended, or revoked when a workload changes or its authority ends.
- Delegation: Preserve the delegator, purpose, scope, and relevant context when an agent acts on another principal’s behalf.
- Policy and enforcement: Specify which operations are allowed, where authorization is evaluated, and how policy responds when the task, context, or available tools change.
- Human approval: Determine which actions can proceed autonomously and which require a human decision. The sources identify binding human and agent identities for human-in-the-loop authorization as an open design question, not a universally settled control.
- Observability and remediation: Record activity across the agent and the services it uses, and define how operators investigate, contain, or remediate unexpected behavior.
- Governance: Set requirements appropriate to the organization’s risk, sector, jurisdiction, and operational constraints. The IETF AIMS draft leaves policy representation and compliance criteria deployment-specific.
How to evaluate an agent identity design
Use these questions to assess an architecture or deployment proposal. They focus on the relationship between identity, authority, and evidence rather than on a particular vendor’s feature list.
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- Can each workload be distinguished? Check whether services can identify the specific agent that made a request, rather than seeing only a shared human account or a generic application credential.
- Can you trace credentials through their lifecycle? Establish how an agent credential is bound to its identifier, provisioned, rotated, and revoked, and what happens when an agent is replaced or its purpose changes.
- Does delegated authority retain its context? Verify that the authorization decision and the resulting record can identify the delegator and relevant purpose and scope, as well as the acting agent.
- Are permissions appropriately scoped? Assess whether an agent can access only the tools and resources required for its task, and how permissions change when its context or available tools change.
- Can an execution chain be reconstructed? Determine whether records connect the initiating authority to the agent’s actions across the models, tools, and services involved, and what protects those records against undetected alteration.
- Does enforcement fit the deployment? Map how identity providers, OAuth-based authorization, workload identity, policy engines, and runtime enforcement points work together in the actual environment.
- Does the control model fit governance needs? Check that approval requirements, audit expectations, and compliance obligations match the relevant jurisdiction, sector, and risk tolerance.
What strong authentication, least privilege, and auditability mean here
NIST’s February 2026 concept paper asks, “What constitutes a strong authentication for an AI agent?” It also asks how to establish least privilege, handle delegation, bind agent identity to human identity for human-in-the-loop authorization, and make action and intent logs tamper-proof and verifiable. These are active implementation and standards questions; the paper does not establish one universal recipe or certification.
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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 glitchesFor an organization, the practical task is to define what assurance is necessary for its particular agents and resources, how authority is narrowed to the intended work, and what evidence would be adequate to review a consequential action. Those decisions depend on deployment and risk. The reviewed guidance does not specify a single authentication method, permission format, or audit mechanism that fits every case.
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Why prompt injection changes the authorization problem
An agent may use tools while processing untrusted or manipulated context. NIST’s concept paper asks what controls can prevent direct and indirect prompt injections and what can limit their impact if they occur. Identity checks alone do not prevent prompt injection, and the current reviewed sources do not settle a complete mitigation recipe.
Identity and authorization still matter because they can help bound the resources and actions available to an agent, while monitoring and remediation can help detect and respond to misuse. They should be treated as parts of a broader control design, not as proof that an agent’s inputs or decisions are safe.
What current guidance does—and does not—establish
| Source | Date and status | What it contributes |
|---|---|---|
| NIST NCCoE announcement | February 5, 2026; announcement of interest in a project, not a completed outcome | Describes interest in applying identity standards and best practices to software agents, particularly agentic AI applications. |
| NIST NCCoE concept paper | February 2026; project concept, not a final standard or certification | Raises questions about agent identification, authentication, authorization, auditability, non-repudiation, delegation, and prompt-injection controls. Its public feedback deadline was April 2, 2026, which has passed. |
| IETF draft-ietf-wimse-aims-00 | September 15, 2026; Internet-Draft, not an RFC or final standard | Proposes a conceptual AI Identity Management System composed from existing standards and distributed across identity and enforcement functions. Draft versions can change. |
| IETF draft-klrc-aiagent-auth-03 | July 6, 2026; Internet-Draft and work in progress | Describes best current practices across agent identity, credentials, provisioning, authentication, authorization, observability and remediation, policy, and compliance. |
| OpenID Foundation, Identity Management for Agentic AI | Prepared beginning in April 2025; background report | Provides background to the identity-management discussion; it should be read separately from current drafts and final standards when determining implementation status. |
On February 5, 2026, NIST said: “The NIST National Cybersecurity Center of Excellence is interested in launching a project to demonstrate how identity standards and best practices can be applied to software agents, with a focus on agentic AI applications.” The statement describes interest in a project, not a completed demonstration or a new standard.
At the September 2026 date of the latest reviewed AIMS draft, the work describes a way to organize identity and authorization capabilities using existing standards. It does not make a single agent protocol a complete solution. Implementations still have to settle deployment-specific policy, credential lifecycle, enforcement, evidence, and governance choices.
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