Agentic Identity.
The identity model for AI agents acting autonomously or on behalf of users — a third category of identity alongside human users and traditional non-human (service) identity, with its own authentication, authorization, and audit patterns.
Agentic identity is the third leg of the identity stool: human (CIAM), workload (NHI), agent. Conflating any two of these is the root of the worst agentic-AI security incidents — an agent running with a user's full token (treating the agent as the user) means the agent's bugs become the user's compromises; an agent running as a generic service account (treating the agent as a workload) loses the audit trail of which user requested which action.
Common questions
What's the difference between agentic identity and NHI?
How do AI agents authenticate to APIs?
Why does agentic identity need its own discipline?
Related terms
In the guides
Authentication for AI Agents: OAuth Patterns for Non-Human Identity
How AI agents authenticate in 2026. The on-behalf-of pattern, delegated agent identity, OAuth 2.1 Dynamic Client Registration, and where the patterns are still being invented.
Authorization Patterns for Agentic Workflows: Delegation, Constraints, and Just-in-Time Permissions
AI agents need authorization models that handle delegated permissions, multi-step workflows, and least-privilege at machine speed. The patterns that work and the ones being invented.
MCP Server Identity Model: Authentication, Authorization, and Trust for the Model Context Protocol
Model Context Protocol is OAuth 2.1 with discovery. How MCP servers register, authenticate clients, scope access, and where the protocol leaves identity questions to the implementer.
Token Management for AI Agents: Lifetimes, Rotation, and Revocation at Machine Speed
Agent tokens are stolen faster and used harder than human tokens. How to set lifetimes, rotate refresh tokens, scope per-tool, and detect anomalies in production agent deployments.