What is the difference between acting as an agent and on behalf of a user?

Why This Matters for Security Teams

The difference between acting as an agent and on behalf of a user is not semantic. It determines which identity is accountable for the action, which permissions are in scope, and how evidence is preserved for audit and incident response. When a gateway collapses those two modes, teams can accidentally grant broad machine authority to workflows that should remain user-scoped, or they can break automation by forcing every agent step through a human token.

This matters even more in agentic systems, where the request path can branch, chain tools, and change intent mid-flight. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point to the same operational risk: authorization must reflect the actual actor and context, not just the front-end session.

NHI Management Group’s Ultimate Guide to NHIs — 2025 Outlook and Predictions notes that NHIs outnumber human identities by 25x to 50x in modern enterprises, which means this distinction affects far more than a single gateway pattern. In practice, many security teams discover the identity mismatch only after a tool call has already written data, rather than through intentional design of the request path.

How It Works in Practice

Acting as an agent means the system presents its own workload identity to downstream services, and those services evaluate the request as machine-initiated work. On behalf of a user means the gateway preserves the upstream human context, often by forwarding a user token or an equivalent delegation artifact, so downstream systems can enforce user-specific policy, data boundaries, and accountability. The practical difference is who the operation is attributed to, and which trust decisions follow from that attribution.

In mature designs, the gateway does not rely on static role assumptions. Instead, it combines workload identity, request context, and policy evaluation at runtime. That may include short-lived credentials, token exchange, and explicit delegation scopes. The goal is to prevent a long-lived agent identity from inheriting human permissions by default. This aligns with current guidance from the CSA MAESTRO agentic AI threat modeling framework, which treats agent behavior as dynamic and tool-driven rather than fixed by a single role.

• Use acting-as-agent for autonomous jobs, scheduled workflows, and system-to-system actions.

• Use on-behalf-of-user when the user’s data rights, approval, or audit trail must remain intact.

• Prefer short-lived delegated tokens over reusable static secrets, especially for high-risk tool access.

• Log both the originating user and the operative workload identity so investigations can reconstruct intent and execution.

The NHIMG research on CoPhish OAuth Token Theft via Copilot Studio shows why this matters: once a workflow can abuse delegated access, the security boundary is no longer the UI, it is the token and the policy behind it. These controls tend to break down when one gateway must mix user-scoped data access with autonomous tool execution in a legacy app that cannot separate delegation paths cleanly.

Common Variations and Edge Cases

Tighter delegation control often increases implementation complexity, requiring organisations to balance clean attribution against developer friction and integration overhead. The hardest cases are hybrid flows, where an agent starts by acting on behalf of a user and then continues as an autonomous agent after the user leaves the session. Best practice is evolving here, and there is no universal standard for this yet.

One common edge case is approval-driven automation. A user may approve a request, but the agent still performs the downstream calls as itself. In that case, the approval is user-originated, but the execution identity is still the agent. Another edge case is shared service accounts, where multiple agents use the same credential. That pattern weakens auditability and obscures accountability, especially if a later incident requires proving which agent executed which action.

For high-risk environments, the cleaner model is to bind the action to a workload identity with explicit context, then issue just-in-time access only for the duration of the task. Where that is not possible, teams should at minimum preserve the user context in logs and policy decisions, and avoid reusing a human token across unrelated agent steps. NHI Management Group’s OWASP NHI Top 10 research and the MITRE ATLAS adversarial AI threat matrix both reinforce that agent behavior and identity abuse must be handled as runtime risks, not static IAM labels.

Related resources from NHI Mgmt Group

• What is the difference between human identity governance and AI agent governance?
• What is the difference between governing human access and governing AI agent access?
• What is the difference between managed identities and hardcoded secrets for AI agents?
• What is the difference between workload identity and API keys for AI agents?