How should teams decide which credential type an AI agent should use?

Choosing a credential type starts with the agent’s authority model

An AI agent should not receive a credential because it is convenient. It should receive the credential that matches who it is acting for, what it is allowed to do, and how long that authority should last. That distinction drives whether you can attribute actions correctly, limit blast radius, and revoke access cleanly when the workflow changes.

For delegated personal actions, the credential should express the user relationship, not a shared background service. For shared operational workflows, use an organisational credential with explicit scope and review. For backend integration, short-lived machine-to-machine access is the safer pattern because it aligns privilege with runtime need instead of persistent trust.

The practical question is whether the agent is exercising borrowed authority, group authority, or service authority. Once teams answer that, the credential choice usually becomes clearer because the credential must reflect the operating model, not the interface the agent happens to use.

Match the credential to delegation, reuse, and revocation

User-scoped credentials fit situations where the agent is genuinely acting on a person’s behalf, such as filing a request, drafting a response, or reading data the user could already access. The important boundary is that the agent should not silently accumulate broader power than the person intended. If the workflow is shared across a team, an organisational credential can be appropriate, but it needs tighter entitlement review because the actions are no longer tied to one individual’s context.

Short-lived M2M tokens are usually the best default for backend-to-backend work because they reduce standing access and make revocation simpler. They are especially useful when the agent is calling APIs, queues, or internal services on a schedule or in response to an event. The credential choice should also reflect whether the agent needs to act once, repeatedly, or continuously; repeated use is where long-lived secrets tend to become a governance problem.

This is why credential strategy is inseparable from AI Agent Authorisation Guide style thinking, where least privilege, task scope, and per-action decisions are the core control points. It also aligns with delegation mechanics described in the Agentic AI Identity Guide, especially when the agent must carry a user context across systems. If teams ignore lifecycle and revocation, the “right” credential becomes unsafe simply because it outlives the job it was meant to do.

Auditability and containment matter as much as the credential format

A credential is not just an access artifact, it is an accountability mechanism. Teams should prefer the type that preserves attribution at the point of action, because investigations become much harder when a shared token or over-broad service credential hides which human request, workflow, or system triggered the call. The same decision also affects containment: if one agent instance is compromised, the ideal credential should limit the damage to a narrow function or time window.

That is why identity context, token exchange, and scoped delegation matter when the agent is operating on behalf of someone else, and why backend services benefit from short-lived tokens that can be rotated or invalidated without breaking unrelated workflows. RFC 8693: OAuth 2.0 Token Exchange is useful when teams need on-behalf-of delegation without copying user secrets into the agent. For pure service access, RFC 6749: The OAuth 2.0 Authorization Framework remains the baseline reference for machine-oriented access patterns.

Risk and Threat Considerations

The main risk is choosing a credential type that expands authority beyond the agent’s real function. Over-scoped or long-lived credentials make it easier for a compromised agent, misconfigured workflow, or careless integration to act with excessive privilege, and they make clean revocation much harder. Shared credentials also blur attribution, which slows containment and weakens accountability when something goes wrong.

Failure mechanism: Teams treat the credential as an implementation detail and reuse a token or key across users, workflows, or environments. That creates credential reuse, privilege sprawl, and weak separation between delegated actions and system actions, so one compromise can affect many workflows.

Impact: The agent can perform unauthorized actions, the blast radius becomes harder to bound, and incident response loses the ability to answer who authorised what. In practice, the most dangerous failure is not a single bad token, but a credential model that makes safe revocation and precise attribution impossible.

Practitioner Guidance

Decision rule: If the agent must be explainable as “doing this for a person,” use user-scoped delegation with explicit approval boundaries. If it is doing repeatable team work, use an organisational credential with reviewable scope. If it is calling services in the background, prefer short-lived M2M access and avoid embedding durable secrets in the agent runtime.

What to verify: Confirm that the credential’s lifetime, scope, and revocation path match the agent’s actual operating model, not the convenience of the implementation. Also verify that logs can distinguish user-driven actions from system-driven actions, because attribution failures often appear first during incident review, not during normal operation.

Practitioner takeaway: The right credential is the one that keeps privilege, attribution, and revocation aligned with the agent’s real role; if those three do not line up, the credential model is already wrong.

Related resources from NHI Mgmt Group

• How should teams govern AI agents that use MCP?
• How should security teams govern AI agents that use OAuth access?
• How should security teams use AI in secret scanning without creating new blind spots?
• How should teams think about AI agent privileges?