OAuth vs. OIDC for AI Systems: Complete Guide (prefactor.tech)

This guide explains when to use OAuth 2.0 vs OpenID Connect (OIDC) for AI systems and why it matters as AI agents proliferate. OAuth 2.0 is an authorization protocol that issues access tokens for machine-to-machine (M2M) access—commonly via the client credentials flow—so agents get scoped, revocable access without exposing long-lived secrets. OIDC builds on OAuth by adding an identity layer (ID tokens, usually JWTs) and is required when agents act on behalf of users or when identity assurance and compliance are needed (e.g., authorization code flow with PKCE). The distinction matters: many organizations deploy AI agents (51%) but lack governance (44%), and credential compromise and unintended agent behavior are real risks—86% of breaches relate to stolen credentials. Technically, OAuth remains essential for scalable, low-latency backend access, while OIDC provides the accountability needed for user-delegated workflows. OAuth 2.1 strengthens security (PKCE, stricter bearer-token rules), but OAuth’s static scope model and limited identity claims mean teams must add policy enforcement, strong client auth, ephemeral credentials, and continuous monitoring. Best practice is to combine both: OAuth for backend resource access and OIDC for identity-critical paths. Platforms like Prefactor implement these patterns with a Model-Context-Protocol approach, policy-as-code, and audit-ready provisioning to simplify secure, auditable agent identity and authorization at scale.