AI agent identity: registering, scoping, suspending and logging AI agents

When an AI system only advises, identity is a question about the people who use it. When an AI agent acts, by calling tools, changing records and talking to other systems, the agent itself needs an identity. Without one, its actions arrive under a shared service account or a borrowed human session, and nobody can say afterwards which agent did what, under whose authority, or how to stop it. This guide covers how to register agents, issue and scope their runtime identities, log what they do, and suspend or revoke them when needed.

Why agents need their own identities

Identity and access management was built for two kinds of actor. People, who log in and are accountable. And services, which run fixed code with fixed permissions. Agents fit neither. They act with some autonomy like a person, run at machine speed like a service, and choose their next step from context rather than fixed code. NIST’s National Cybersecurity Center of Excellence made the same point in its February 2026 concept paper on software and AI agent identity and authorisation. Existing frameworks were designed for human users and static software, and agents behave like neither.

Three things go wrong when agents borrow identities.

• Attribution fails. If five agents and a batch job share one service account, the logs cannot tell you which one changed a customer record.
• Least privilege fails. A shared account carries the union of everything its users need, so every agent on it can do everything any of them can.
• Revocation fails. You cannot stop one misbehaving agent without stopping everything else on the same credential.

OWASP’s Top 10 for Agentic Applications lists identity and privilege abuse among the leading risks for agentic systems. Agents that inherit cached user sessions, API keys or tokens from earlier tasks can reach far beyond what their purpose needs.

Two halves of an agent identity

It helps to separate two things that are often blurred.

The registration is the governance record. It says what the agent is for, who owns it, which platform it runs on, which models it uses, which tools it may call, which actions it may take on its own and which always stop for a person, and how it is suspended. It lives in your AI register and changes through your change process.

The runtime identity is the credential the agent presents when it acts. Depending on your environment, that may be a workload identity, an OAuth client, a cloud service principal, a signed token or an API key. It lives in your identity provider, secrets manager or platform, and systems check it on every request.

Good agent identity binds the two. Every runtime identity maps to exactly one registered agent, and every registered agent lists the runtime identities it presents. That binding is what lets a log line become an accountable statement. This agent, owned by this person, did this, using these permissions.

Step 1: Register the agent

Create a register entry before the agent goes live, through the same intake as any other AI. Record its purpose, the business unit, a designated owner, an executive accountable and a technical contact. Record the platform it runs on and the models it uses. List the tools it may call by category, such as database, API, code execution, file system, web and messaging. Then write the action boundary. What it may do without a person, what always needs approval, and what it must never do.

Step 2: Issue a dedicated runtime identity

Give the agent its own credential, separate from any person and any other system. Issue separate identities per environment, so a test agent cannot act in production. Where the platform supports it, prefer short-lived credentials issued at runtime over long-lived keys stored in configuration. Never let an agent run on a human’s personal session or on a key copied from another integration.

When an agent acts on behalf of a user, make the delegation explicit. Standards such as OAuth 2.0 Token Exchange let a system carry both the agent’s identity and the user it acts for, so downstream systems see both. The record should show the person who asked and the agent that acted.

Step 3: Scope the identity to least privilege

Grant only what the agent’s purpose needs. Read access where it only needs to read. Draft rights where a person should release. Access to the specific tables, mailboxes or APIs it uses, not the whole system. Time-limit elevated permissions to the task that needs them.

Then enforce the scope where the agent acts. A list of permitted tools, enforced at the gateway in front of the agent’s model traffic, can refuse a request that tries to call a tool not on the declared list, or strip the tool and forward the rest. Rate limits and budgets per runtime identity stop a looping agent from running up cost or volume.

Step 4: Bind the identity to the register entry

Record which runtime identities belong to which registered agent, and keep that mapping current as credentials rotate. With the binding in place, any request in any log can be traced to an agent, its version and its owner. Without it, you have credentials nobody can explain.

Step 5: Log decisions under the identity

Record what the agent actually did, attributed to its runtime identity. Each entry should carry when it happened, what the agent decided, the tool it called, the outcome and whether a gateway allowed or blocked it. Flag any tool call that is not on the agent’s declared list. The guide on what belongs in an agent decision log covers the fields in detail.

The log is what lets you answer questions months later. Why did the agent send that message? Which records did it change last March? Was that action within its scope at the time?

Step 6: Refuse what you have not registered

Treat an identity you have not registered as a finding, not a curiosity. An agent presenting an unknown credential at your gateway, or a registered agent calling a tool it never declared, should be blocked or at least raised for a person to look at. Measure it over time. A rising share of undeclared tool calls means the register no longer describes what the agent does.

Step 7: Keep suspension and revocation ready

Every agent needs levers that a named person can pull quickly.

• Suspend or revoke the runtime identity. Every system that honours the credential stops accepting it.
• Re-scope the agent. Remove a tool or an action while leaving the rest working.
• Block at the gateway. Deploy a restricted rule set, make the gateway refuse the agent’s requests whenever a rule cannot be checked (known as failing closed), or tighten its budget.
• Quarantine the entry. Mark the agent as under review so nobody restores it by accident.

Write down who may pull each lever, how fast it takes effect and what happens to work in flight. Then test them. A kill switch nobody has used is an assumption. Decide in advance whether any lever should fire automatically on a clear signal, and who must approve if it does.

Step 8: Review, rotate and retire

Review an agent’s scopes whenever its purpose, tools or models change, and on a schedule set by its risk. Rotate credentials as often as your security policy requires. When an agent is retired, revoke every runtime identity it held and keep the registration and logs, so its history stays answerable.

Multi-agent systems and delegation chains

Agents increasingly call other agents. Each hop should carry identity. The calling agent’s identity, the called agent’s identity and, where relevant, the person at the start of the chain. Do not let a called agent inherit the caller’s privileges by default. Singapore’s Model AI Governance Framework for Agentic AI, updated in May 2026, added guidance on multi-agent systems and third-party agents, and treats identity and permission controls as part of bounding an agent’s risk up front.

Third-party agents, such as a vendor’s agent acting in your systems, need the same treatment. Register them, give them identities you issue and control, scope them, and log what they do.

What the frameworks say

• Singapore Model AI Governance Framework for Agentic AI (IMDA, January 2026, updated May 2026). Calls for identity and permission controls and limits on agents’ access to tools and systems, as part of assessing and bounding risk up front.
• OWASP Top 10 for Agentic Applications (December 2025). Lists identity and privilege abuse as a leading risk, with least-privilege agent identities among the mitigations.
• NIST NCCoE concept paper (February 2026). Scopes a project on identification, authorisation, auditing and non-repudiation for software and AI agents.
• EU AI Act. For high-risk systems, requires logging that makes the system’s operation traceable and human oversight that can intervene or stop it.
• ISO/IEC 42001. Asks for defined roles and responsibilities, documented AI system information and controls over the AI system lifecycle, all of which an agent’s registration supports.

Where Aicura fits

The binding in Step 4 is built into the agent’s page in Aicura’s AI Register. At Pro, that page has an Identities tab listing the runtime identities the agent presents, beside a Tools tab for the tools it declares, so the registration and the credential sit together. Budgets and rate limits can be counted against each runtime identity rather than the agent as a whole, and gateway rules decide what happens to an agent nobody registered.

The levers in Step 7 exist as responses in Guardian, the part of Aicura that acts when monitoring spots a problem. These are revoking a runtime identity, refusing undeclared runtime identities, re-scoping the agent and quarantining it, each set to tell you, wait for a named approver or act. Which lever fires, and when, stays the owner’s decision.