AI Agent Identity: How to Secure and Manage AI Agents

AI agents can do more than generate responses. They can query databases, create tickets, call APIs, update records, and coordinate with other agents to complete a task. But as these systems gain access to enterprise resources, one security question becomes harder to ignore: how do you know which agent performed an action, whose request it was acting on, and whether it had permission to do so?

Most authentication systems were designed around human users and applications. AI agents do not fit neatly into either category. An agent can act autonomously, choose tools at runtime, and execute multiple operations on behalf of different users. Giving it a human user's credentials or a shared service account makes it difficult to track and control those actions.

This is where AI agent identity comes in. It gives every registered agent a verifiable identity, allowing security teams to authenticate the agent, define its permissions, and trace its actions without losing sight of the person or service behind a request.

This guide explains what AI agent identity is, how it differs from traditional non-human identities, and how to implement it across enterprise AI systems.

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What Is AI Agent Identity?

AI agent identity is the verifiable credential a registered agent presents on every call it makes. It answers a question tokens alone cannot: not "was a valid credential presented" but "which agent is calling, and who is it acting for right now."

TrueFoundry already recognizes two kinds of principal, and an agent identity is a third.

The line that matters is delegation. A virtual account can only ever act as itself, so point one at an MCP server and every call looks the same no matter who prompted it. An agent identity can act on behalf of someone else, so a single registered agent that serves hundreds of people still makes calls that stay attributable to whoever each request is for, while remaining identifiable as the agent. That is the property that makes agent traffic auditable.

How does Agent Identity Differ From Human and Service Identity?

Human identity, service identity, and agent identity serve different purposes, even when they interact within the same workflow.

• Human identity establishes who initiated a request. It is tied to a person and their assigned permissions.
• Service identity establishes which application or service is making a request. It is commonly used for application-to-application communication.
• Agent identity establishes which AI agent is executing a request and supports delegation when the agent acts on behalf of a user or service.

Consider an employee asking an AI agent to investigate a production issue. The agent queries an internal database, retrieves a Jira ticket, and prepares a suggested fix. The employee's identity establishes who initiated the task. The agent's identity establishes which agent performed the tool calls. Delegation context connects those actions to the employee's request.

Without this separation, logs may identify only the employee or a shared service account, leaving security teams without a reliable way to distinguish the agent responsible for an action.

What does an AI Agent Identity Include?

An AI agent identity needs more than a name or identifier. It must provide enough information to establish the agent's identity, determine whether it can access a resource, and maintain accountability across delegated requests.

Depending on the identity implementation, this can include:

• Agent identifier: A stable identifier that maps the caller to a registered agent.
• Authentication credentials: A verifiable token or other credential that establishes the agent's identity.
• Ownership and registration metadata: Information about the agent's purpose, owner, and lifecycle.
• Access permissions: Policies defining which models, tools, APIs, or data the agent can access.
• Delegation context: Information identifying the user or service the agent is acting for, where applicable.
• Audit context: Claims and request metadata that help trace actions back to the agent and the originating request.

Not every implementation carries all these fields in the same token. Some information may live in the agent registry or be supplied through a separate authorization mechanism.

The important requirement is that authentication and authorization work together. A valid credential establishes identity; it does not automatically grant access to every resource. The system must also check the agent's permissions and, where delegation is involved, the authority associated with the originating request.

Why Every Agent Needs Its Own Identity

Giving each agent a distinct identity is one decision that unlocks the whole governance model.

• Attribution. The receiver of a call can tell whether the caller is a human, a service, or a specific agent. Actions become provable after the fact, which is what an audit actually requires.
• Per-agent policy. One person drives many agents, and they should not all inherit that person's full reach. Jane's support copilot that reads Jira and her engineering agent that writes to it are different principals, even though both act for Jane. Distinct identities let you scope them differently.
• No anonymous agents. If an agent can only reach a tool by presenting a registered identity, then registration becomes the enforcement point. You get an org-wide inventory for free, and you can revoke one rogue agent without touching the rest.

That last point is the quiet payoff. Once identity is required to act, the registry stops being documentation and becomes the chokepoint that makes every other control possible.

How does AI Agent Identity Work?

AI agent identity works through a series of steps that establish which agent is making a request, verify its identity, determine what it can access, and track the actions it performs. These steps connect identity with access control throughout an agent's workflow.

Identification

Identification assigns each registered agent a distinct identity. This identity connects the agent to its registration details, owner, and intended purpose. When a request reaches a protected resource, the system can use this identifier to determine which agent is making the call.

Authentication

Authentication verifies that the agent is who it claims to be. Depending on the implementation, the agent may present a signed token or another supported credential. The receiving system validates the credential and checks relevant claims, such as issuer and audience, before accepting the request.

Authorization

Authorization determines what the authenticated agent is allowed to do. Policies can restrict access to specific models, APIs, MCP servers, tools, or data. A valid identity does not automatically grant permission; the system must evaluate the requested action against the applicable access rules.

Delegation

Delegation allows an agent to act on behalf of a user or another service without losing its own identity. The system evaluates the agent's authority alongside the delegated context to determine whether the requested action is permitted. This helps preserve accountability when one agent serves multiple users.

Audit and Enforcement

Audit and enforcement ensure that identity and access policies apply throughout the workflow. Gateways can validate requests, enforce permissions, and record relevant activity. Audit logs should connect actions to the agent and, where available, the originating user. Security teams can use these records to investigate incidents, review access, and revoke compromised identities.

What are the Security Risks Associated With AI Agent Identity?

AI agents use enterprise credentials to access tools, APIs, and sensitive data. Without proper identity controls, they can misuse permissions, follow malicious instructions, or operate without oversight.

Over-privileged access

Agents may receive broader permissions or long-lived API keys than their tasks require. If compromised, these credentials can expose sensitive systems.

How to reduce these risks: Apply least-privilege access, restrict credentials to specific resources, use short-lived tokens where supported, and review permissions regularly.

Inherited permissions

Agents using shared service accounts or human credentials can inherit excessive access. This also makes it harder to identify which agent performed an action.

How to reduce these risks: Assign each agent a distinct identity. Evaluate the agent's permissions separately from the user's permissions, and restrict delegated access to what the task requires.

Prompt injection and confused deputy

Malicious instructions hidden in emails, documents, or webpages can trick authenticated agents into misusing their legitimate permissions to perform unauthorized actions.

How to reduce these risks: Treat external content as untrusted, validate tool calls, enforce authorization outside the model, and require human approval for sensitive operations.

Orphaned and shadow agents

Forgotten or unapproved agents may retain active credentials after their original purpose ends, leaving enterprise resources exposed without clear ownership or oversight.

How to reduce these risks: Maintain a central agent registry, assign clear ownership, review inactive agents regularly, and revoke credentials when agents are retired.

Identity spoofing and inter-agent abuse

Weak authentication between agents can allow rogue agents to impersonate trusted workloads or interfere with delegated tasks, potentially exposing data or triggering unauthorized operations.

How to reduce these risks: Authenticate each agent with verifiable credentials, validate token claims, and authorize agent-to-agent requests before allowing access to tools or resources.

Behavioral drift

An agent's behavior may change as its instructions, tools, or environment evolve. Without runtime monitoring, it can perform actions outside its intended role while using valid credentials.

How to reduce these risks: Monitor tool calls and access patterns, alert on unusual activity, review permission changes, and enforce fixed authorization policies regardless of the agent's decisions.

Agent Identity, MCP and Agent-to-Agent Communication

AI agents rely on tools and other agents to complete tasks, making identity verification important at every connection. The Model Context Protocol (MCP) standardizes how agents connect to external tools and data sources, while agent-to-agent communication allows agents to delegate work to one another.

In both cases, identity helps establish who is making a request and whether that caller is authorized to perform the requested action.

With MCP, an agent connects to an MCP server to access tools, resources, or prompts. Authentication identifies the caller, while authorization determines which operations it can perform. An MCP gateway can enforce these policies centrally, helping prevent unregistered agents from accessing protected tools.

Agent-to-agent communication introduces another layer of delegation. When one agent asks another to perform a task, the receiving agent needs to verify the calling agent's identity and check the permissions associated with the request. Where the task involves a user, the system should preserve that delegation context rather than treating the first agent's access as unrestricted authority.

A secure implementation should authenticate each participant, validate credentials and permissions at every trust boundary, and maintain audit records for delegated actions. This allows teams to trace activity across MCP tool calls and multi-agent workflows without relying on shared credentials or blindly trusting one agent because another agent invoked it.

H2: How does Agent Identity Support Zero Trust Security?

Zero trust security follows a simple principle: never trust a request solely because it comes from an internal system or an authenticated user. Every request must be verified and authorized. AI agent identity applies this principle to autonomous agents accessing enterprise tools, APIs, and data.

A distinct identity for each agent helps enforce zero trust in several ways:

• Verify every agent: Authenticate the agent before allowing it to access protected resources, regardless of where it runs.
• Enforce least privilege: Grant access only to the tools, data, and actions required for the agent's task.
• Validate delegated access: Check both the agent's identity and the authority of the user or service it acts for.
• Monitor activity continuously: Track tool calls, access patterns, and permission changes to identify suspicious behavior.
• Revoke access when needed: Disable compromised or retired agents without disrupting unrelated identities.

For example, an AI agent may be authorized to read Jira tickets but not delete them. Even if the agent has a valid identity, a request to delete a ticket should be denied unless its permissions explicitly allow that action.

With agent identity and gateway-level enforcement, organizations can apply zero-trust policies consistently across AI agents, MCP servers, and connected enterprise services.

H2: What Standards are Being Used to Guide AI Agent Identity Management?

No single standard currently covers every aspect of agent identity, delegation, authorization, and auditing.

The key standards and frameworks include:

• OAuth 2.0 and OpenID Connect (OIDC): OAuth supports delegated access to APIs, while OIDC provides an authentication layer. Together, they help establish identity and authorization context in agent workflows.
• SPIFFE/SPIRE: SPIFFE defines a framework for assigning verifiable identities to workloads across different environments. SPIRE implements SPIFFE and can issue workload credentials, helping agents authenticate to other services without relying on long-lived static secrets.
• NIST AI Risk Management Framework (AI RMF): This voluntary framework helps organizations identify, assess, and manage AI risks. It provides broader governance guidance that complements agent identity and access controls.
• NIST Software and AI Agent Identity and Authorization initiative: NIST is exploring standards-based approaches to identifying agents, managing their identities, and authorizing their actions. This work addresses challenges such as delegation, accountability, and secure access to enterprise resources.

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How TrueFoundry Issues and Governs Agent Identity

On TrueFoundry, an agent identity is not a separate object you create and bolt on. It is a step in registering the agent, so the identity and the agent are one to one. Register the agent and its identity comes into being. Delete the agent and the identity goes away with it.

Where the credential comes from

During registration you choose how the agent proves who it is:

• TrueFoundry-backed. TrueFoundry issues and signs the agent's token. This is the simplest option and the right default for agents you build and run yourself.
• Identity provider-backed. The agent authenticates with a token from your own provider, such as Okta, Microsoft Entra, any OIDC provider, or a SPIFFE and SPIRE endpoint. You map specific claim values to the agent so an incoming token resolves to it. This path unlocks On-Behalf-Of (OBO) delegation, so the agent can carry a user forward through the chain.

The identity means the same thing either way. Only the issuer differs, and you can mix issuers per agent inside one tenant.

TrueFoundry as the identity broker

When your identity provider has no agent-identity capability yet, TrueFoundry can play all three control-plane roles. It issues each agent's identity, authorizes every call at the Agent Gateway and MCP Gateway, and, with token exchange, mints a scoped credential for each hop. Your enterprise SSO keeps doing what it already does, which is authenticating human users. On a call, the agent presents its own identity alongside the user's, so both are visible at the same time:

curl https://gateway.truefoundry.ai/api/llm/chat/completions \
  -H "Authorization: Bearer $USER_TOKEN" \
  -H "x-tfy-agent-authorization: $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "openai-main/gpt-4o",
        "messages": [{"role": "user", "content": "Summarize my open issues"}]
      }'

The agent fetches its own token from the registry (the Get Token action on the agent), the same way a virtual account does.

Who an agent is allowed to act for

Delegation is not unbounded. It is scoped by the agent's collaborators, set on the Access Control step of registration. The same list that decides who may invoke the agent also decides who the agent may act for.

An Agent Manager can edit the agent. Agent Access can invoke it and be acted for by it. An Owner team, set separately, stays accountable for the agent from creation to retirement. Those grants are the agent's defined authority: which people it may act for, and which MCP servers and models it may reach.

Why is Agent Registration an Enforcement Point

The reason to register agents in one place is not tidiness. It is that the gateways deny any caller that presents no registered identity, so the Agent Registry doubles as the enforcement point. Nothing acts anonymously, and the inventory is complete by construction.

Each registry entry carries four things: a verifiable identity, an accountable owner, a defined authority (the tools, models, and people it may reach or act for), and a lifecycle that covers credential rotation and a deprovisioning path. Whatever the agent is built on, Bedrock, Vertex AI, LangGraph, a custom HTTP service, or a copilot embedded in a SaaS product, it enters governance by being registered, with nothing to move or rewrite.

Because identity is required to act, revocation is surgical. Pull one agent's identity and it stops reaching every model and MCP server at once, while every other agent keeps running. That is the kill switch that a shared service account can never give you.

How do Agent Identities and Virtual Accounts Work Together?

Agents rarely act alone. The applications and services around them still need machine identities, and that is what virtual accounts are for. A virtual account is a non-user identity that an application or service assumes to reach resources, with a token you can auto-rotate, set rotation alerts on, and sync to your secret manager. The guidance is to create one virtual account per application and scope it to the minimum permissions it needs.

Put together, you get a clean separation of principals: people authenticate through your SSO, services and apps carry virtual accounts scoped to least privilege, and agents carry their own agent identities that can act for those people and services without ever losing attribution. Access decisions in all three cases are enforced at the gateway, and every call is inspected by AI agent guardrails for what it actually contains.

Note: Agent Identity on TrueFoundry is currently in beta, and gateway-issued per-hop token exchange is rolling out. Behavior may change as the feature matures.

Secure, Govern, and Manage AI Agents

An agent is not a person and it is not a static service, so it should not borrow either one's credentials. Give every agent its own AI agent identity and three hard problems get easier at once: you can attribute every action, scope each agent independently of the human driving it, and revoke a single rogue agent without downtime for the rest. On TrueFoundry that identity is created at registration and enforced at the gateway, so non-human identity stops being a spreadsheet of shared keys and becomes real infrastructure.

See how TrueFoundry issues and governs agent identity across your models, tools, and agents from one control plane. Book a demo today.