Authorization Code with PKCE is for agents acting on behalf of a user, so the flow includes browser-based login, consent, and both ID and access tokens. Client Credentials is for autonomous machine-to-machine access, where the agent authenticates with its own credentials and receives only an access token. The practical difference is whether a user identity must be represented.
Why the Flow Choice Matters for AI Agents
authorization code with pkce and Client Credentials solve different trust problems. For AI agents, that difference is not academic: it determines whether the agent is acting as a delegated user, or as an autonomous workload with its own service identity. That choice affects consent, auditability, token scope, revocation, and whether downstream actions can be tied back to a person or only to the agent itself.
In agentic environments, the wrong flow often shows up as an access-design problem first and a security problem second. If a task needs user context, the agent should inherit only the minimum delegated rights needed for that session. If the task is machine-run and userless, the agent should not be forced through a user-consent pattern that hides the real identity model. SailPoint reports that 80% of organisations have already seen AI agents act beyond intended scope, which is why the identity model matters as much as the model behaviour itself. AI Agents: The New Attack Surface report
In practice, many security teams discover the mismatch only after an agent has already been granted broader access than the task actually required.
How It Works in Practice
Authorization Code with PKCE is the right pattern when the agent must operate on behalf of a signed-in user. The user authenticates interactively, the agent receives a delegated grant, and the resulting access token should reflect user-linked permissions, not an independent machine entitlement. That makes sense for workflows like drafting an email, summarising a ticket, or calling a business app where the user remains the principal.
Client Credentials is different. It is designed for machine-to-machine access where the agent authenticates as itself, typically with a client secret, certificate, or other workload credential, and obtains an access token without a user session. That makes it the better fit for background jobs, orchestrators, retrieval services, and other autonomous components that need stable service-to-service authentication. For those cases, workload identity becomes the real unit of trust, and long-lived static secrets should be avoided where short-lived credentials are possible.
- Use Authorization Code with PKCE when the action must be attributable to a user and should inherit user consent.
- Use Client Credentials when the agent is a service principal performing a bounded machine task.
- Keep token lifetime short, because agentic systems can chain calls quickly and expand blast radius fast.
- Scope access to the minimum downstream resources the agent actually needs, not the widest app permission set.
OWASP’s agentic guidance is useful here because it frames the control problem around autonomous behaviour, not just login mechanics. OWASP Agentic AI Top 10 NHIMG’s guidance on dynamic versus static secrets is also directly relevant when teams decide how an agent should present its own credentials over time. Ultimate Guide to NHIs — Static vs Dynamic Secrets
These controls tend to break down when an organisation reuses one OAuth pattern for both human-delegated actions and autonomous service tasks, because the audit trail and privilege model no longer match the way the agent actually behaves.
Common Variations and Edge Cases
Tighter identity separation often increases integration overhead, requiring teams to balance clean delegation against operational simplicity. The hardest cases are hybrid agents that sometimes act for a user and sometimes run autonomously, because there is no universal standard for collapsing those two modes into one permission model.
Current guidance suggests treating hybrid behaviour as two distinct trust states rather than one combined client. If the agent can switch from delegated to autonomous operation, it should not silently retain the same token shape, same scope, and same revocation assumptions across both states. Teams also need to distinguish between consent for data access and authority for action: a user may allow the agent to read content, but not to execute side effects under the same grant.
Edge cases appear when agents call other agents, when background tools refresh tokens on behalf of a user, or when an orchestration layer brokers access across multiple downstream APIs. In those setups, the practical question is not only which flow issued the token, but whether the resulting identity can still be bounded, monitored, and revoked without collapsing the whole chain. For broader agent-risk context, the OWASP and CSA agentic references are helpful complements. CSA MAESTRO agentic AI threat modeling framework
What practitioners underestimate is that the auth flow choice becomes a governance decision once agents start making repeated calls across systems, because the token model quietly defines who is responsible for every action the agent can take.
Risk and Threat Considerations
The main risk is privilege mismatch: a user-delegated flow can accidentally over-attribute autonomous actions to a person, while a machine-to-machine flow can over-grant a service identity that is hard to constrain or revoke. In agentic systems, that mismatch can turn into overscoped access, weak accountability, and poor containment when an agent behaves unexpectedly.
Failure mechanism: Teams often assign a flow based on convenience instead of principal type, then let the resulting token scopes expand across APIs and tools. If an autonomous agent receives user-style delegated access, it can inherit privileges that exceed the real task. If a user-facing workflow is pushed into Client Credentials, the organisation may lose the ability to express consent, intent, and per-user accountability.
Impact: The result is excessive access, unclear attribution, and larger blast radius when the agent makes a bad call, repeats a harmful action, or is abused through a compromised workload credential.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 — Agent Identity and Access Control | Covers delegated vs autonomous agent access and token misuse. |
| Recommendation — Separate user-delegated and service-only agent access paths, and scope tokens to the task. | ||
| OWASP Non-Human Identity Top 10 | NHI-01 — Secrets and Credential Management | Client Credentials depends on workload credentials and token hygiene. |
| Recommendation — Use short-lived workload credentials and rotate or revoke agent secrets aggressively. | ||
| CSA MAESTRO | AIC-3 — Agent Identity and Authorization | Directly addresses how autonomous agents authenticate and are authorised. |
| Recommendation — Bind each agent to a distinct workload identity and enforce intent-aware authorization. | ||
| NIST AI RMF | GOV-2 — AI Governance Policies and Processes | The flow choice is an AI governance decision affecting accountability and access. |
| Recommendation — Define policy for when agents may act on behalf of users versus as autonomous workloads. | ||
| CIS Controls v8 | 6.3 — Access Grant Management | Access grants and account scope must match the agent's operating mode. |
| Recommendation — Review and remove excess agent access grants before deployment and after role changes. | ||
Practitioner Guidance
What to prioritise: Classify the agent by principal first: if the action must be attributable to a person, use delegated user auth; if it is a bounded service task, use workload auth. That decision should come before scope design.
Decision rule: If the agent can complete the task without a live user context, do not preserve a user-style flow just because it is easier to implement. If the user must approve the action or own the outcome, do not hide that relationship behind a service credential.
What to verify: Confirm that token scope, refresh behaviour, and revocation map to the real operating mode of the agent. The key check is whether the system can still explain who authorised the action after the token has been used across several downstream calls.
Practitioner takeaway: The right flow is the one that preserves the true principal of the action; once an agent can both represent a user and act independently, identity design becomes a governance boundary, not just an authentication choice.
Related resources from NHI Mgmt Group
- What is the difference between code scanning and runtime identity monitoring?
- What is the difference between code review and intent alignment for AI agents?
- What is the difference between continuous authorization and login-time authentication for AI agents?
- How do organisations decide between OAuth Authorization Code flow and Client Credentials?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 8, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
