Topic starter
06/06/2026 1:38 am
TL;DR: AI agents in B2B workflows need different credentials depending on whether they act for a user, an org-wide workflow, or backend infrastructure, according to WorkOS. The key governance issue is that agent identities inherit permissions and lifecycle rules differently from both humans and traditional machine accounts, so the wrong primitive expands blast radius.
Editorial analysis by NHI Mgmt Group, based on content published by WorkOS: “Machine identity for AI agents: Which credential to issue and when”.
Key questions
Q: How should teams decide which credential type an AI agent should use?
A: Choose the credential based on who the agent acts for and how it runs.
Q: Why do AI agents need different access rules than human users or backend services?
A: AI agents can act on behalf of a user, across an organisation, or as infrastructure, so one generic identity model does not fit.
Q: What failure mode appears when agents get the wrong credential primitive?
A: The main failure mode is blast-radius expansion.
Practitioner guidance
- Define agent classes before issuing credentials Classify each agent as personal, shared, or backend so the credential primitive matches the authority model rather than the UI or deployment pattern.
- Bind delegation to the correct lifecycle Use user-scoped keys only when offboarding the human should revoke the agent, and avoid reusing human IAM assumptions for shared or service agents.
- Scope shared agents with explicit permissions Limit org-scoped keys to the exact actions the workflow needs, and separate operational ownership from the credential itself.
Bottom line: AI agent credentials should be selected according to the agent's relationship to a human, an organisation, or backend infrastructure, not according to a single default pattern.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
06/06/2026 2:53 am
AI agent credentials are a lifecycle and authority problem, not a token-format problem: the article correctly separates user-scoped, org-scoped, and M2M credentials because the real governance question is who the agent acts for. A personal agent should inherit a human lifecycle, a shared workflow agent should inherit org-wide permission scoping, and a backend agent should inherit service-to-service expiry logic. The practitioner conclusion is that the credential type must match the actor relationship before any implementation choice is made.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organizations manage credentials for AI agents?
A: Organizations should transition from hard-coded credentials to runtime-fetched credentials that enhance security by ensuring that tokens are not stored permanently. Utilizing solutions like the MCP Secret Wrapper can help eliminate the risks associated with static credentials.
👉 Read our full editorial: Machine identity for AI agents: choosing the right credential