AI Agent Identity
Quick Take
- In brief: An agent identity names the automated actor behind a request.
- Useful for: Applying permissions and tracing actions in logs.
- Main limit: A verified identity does not prove trustworthiness.
Overview
If a CI agent changes a repository, the audit trail should distinguish it from the developer who started the job and the service that ran it.
Core Definition
An AI agent identity is a credential or identifier that a system uses to recognize an automated actor.
Background and Evolution
Service identities already support automated software. Agent systems add delegation and tool use, so records may need to connect the agent, user, workflow, and service.
How It Works
A trusted issuer creates an identity credential. A platform verifies it, links it to a principal, and uses that identity in permission checks and audit records.
Applications in Software Development and Testing
Give a coding agent a separate identity for repository access. Logs can then show which agent opened a pull request, ran a test, or changed a file.
Strengths and Limitations
Distinct identities improve accountability and support scoped access. They do not show whether the agent’s code, model, or requested action is safe.
Comparison with Similar Terms
Authentication checks which identity is making a request. Authorization decides what that identity may do. Identity is one input to a trust decision, not the decision itself.
Best Practices
Use unique identities, short-lived credentials where possible, clear issuer and owner records, and a documented revocation process.
Common Pitfalls
Do not share a human user’s token with many agents. Shared credentials hide which actor performed an action.
FAQ
Can an agent be trusted once its identity is verified?
No. Verification identifies a principal; trust also depends on permissions, software provenance, runtime controls, and task risk.