Answer target
What is non-human identity for AI agents?
Updated June 2, 2026
Short answer
Non-human identity for AI agents is the practice of treating an agent as its own actor with its own credentials, access grants, expiry, and audit history. The agent should not simply borrow a human user's full credential set.
Borrowed human identity is hard to audit
If an agent uses a human's credentials, audit history may show that the human acted even when the agent performed the operation. That makes incident review and access cleanup harder.
A named runtime is easier to reason about
A useful agent identity has a name, project assignments, role settings, direct secret grants, key expiry rules, and rotation history. Those pieces let a team answer who can access what without reading every prompt or local file.
This does not need to start as enterprise IAM
The first version can be simple. Most teams need clear agent names, scoped access, direct secret grants, and audit history before they need advanced policy languages or workload-identity federation.
Human credentials vs agent credentials
Human credentials authenticate a person who can make decisions, review context, and manage access. Agent credentials authenticate a non-human runtime that should only perform scoped work. Mixing the two makes audit history less useful and increases blast radius.
Practical workflow
- 1Create one identity per durable agent contextAvoid using one generic token for every tool and project.
- 2Grant access through roles and direct secretsProject roles affect what the agent can see and manage; direct secret grants affect what it can resolve.
- 3Expire or rotate keys on a scheduleAgent keys should be easier to rotate than provider credentials.
Where ScopeHold fits
ScopeHold models agents as first-class runtime identities with project assignment, direct grants, key provisioning, expiry settings, and audit history.