Inferensys

Glossary

OpenID Connect (OIDC)

A simple identity layer built on top of the OAuth 2.0 protocol that allows clients to verify the identity of an end-user based on the authentication performed by an authorization server.
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IDENTITY LAYER

What is OpenID Connect (OIDC)?

OpenID Connect (OIDC) is a simple identity layer built on top of the OAuth 2.0 protocol that allows clients to verify the identity of an end-user based on the authentication performed by an authorization server, and to obtain basic profile information about the end-user in an interoperable and REST-like manner.

OpenID Connect (OIDC) is an authentication protocol that delegates user verification to an identity provider (IdP). It extends OAuth 2.0 by adding an ID Token, a cryptographically signed JSON Web Token (JWT) that asserts the user's identity, enabling relying parties to confirm who a user is without managing passwords.

In agentic systems, OIDC underpins workload identity and continuous authentication for autonomous agents. By issuing scoped tokens, it mitigates the confused deputy problem and prevents agent impersonation attacks, ensuring that an agent acting on behalf of a user cannot exceed its delegated authority within a zero trust architecture.

IDENTITY LAYER

Key Features of OIDC

OpenID Connect extends OAuth 2.0 to add an identity layer, enabling clients to verify end-user identity and obtain basic profile information in an interoperable, RESTful manner.

IDENTITY & ACCESS

Frequently Asked Questions

Clear answers to the most common questions about OpenID Connect, its relationship to OAuth 2.0, and its role in securing autonomous agent communication.

OpenID Connect (OIDC) is an identity layer built on top of the OAuth 2.0 protocol that allows clients to verify the identity of an end-user based on the authentication performed by an authorization server. It works by introducing an ID Token, a JSON Web Token (JWT) that contains claims about the authenticated user. The flow proceeds as follows: the client (Relying Party) redirects the user to the OpenID Provider (OP) for authentication. Upon successful login, the OP issues an ID Token alongside an optional Access Token. The client validates the ID Token's signature using the OP's public keys from the jwks_uri endpoint, verifies the iss (issuer) and aud (audience) claims, and establishes an authenticated session. Unlike pure OAuth 2.0, which is about delegated authorization, OIDC standardizes identity federation, single sign-on (SSO), and userinfo retrieval via the /userinfo endpoint.

IDENTITY PROTOCOL COMPARISON

OIDC vs. OAuth 2.0 vs. SAML

A technical comparison of the three dominant identity federation and authorization protocols used in modern agentic system architectures.

FeatureOpenID Connect (OIDC)OAuth 2.0SAML

Primary Purpose

Authentication + Authorization

Authorization

Authentication + Authorization

Protocol Type

Identity layer on OAuth 2.0

Authorization framework

XML-based federation protocol

Token/Assertion Format

JSON Web Token (JWT)

Access Token (opaque or JWT)

SAML Assertion (XML)

Transport Binding

REST/JSON over HTTP

REST/JSON over HTTP

SOAP, HTTP Redirect, HTTP POST

User Identity Claims

Single Sign-On (SSO)

Mobile/Native App Support

Single Logout (SLO)

Back-channel and front-channel

SOAP-based SLO profile

Discovery Mechanism

OpenID Discovery (/.well-known)

SAML Metadata XML

Dynamic Client Registration

RFC 7591

Token Revocation

Standardized (RFC 7009)

Standardized (RFC 7009)

Proprietary per IdP

Session Management

Session Management spec

SessionIndex in assertions

Cryptographic Agility

JOSE (JWS/JWE/JWK)

JOSE or opaque

XML Signature/Encryption

Modern API Authorization

Enterprise Federation

Agent Impersonation Resistance

DPoP + mTLS constrained

DPoP + mTLS constrained

Holder-of-Key subject confirmation

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.