Secure AI Agent Identity: OAuth 2.0, mTLS, and Didit

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As autonomous AI agents increasingly access APIs and sensitive data, secure machine-to-machine identity, authentication, and authorization become essential, with API gateways, OAuth 2.0 client-credentials flows, granular scopes, and mutual TLS providing layered protection against unauthorized access and interception. The discussion argues that traditional identity-verification services are built around human browser-based workflows and cannot readily support agents that need to register, configure verification processes, manage sessions, and respond to results programmatically. It presents Didit as an AI-native identity platform offering a Model Context Protocol server, agent skills, and APIs that let agents obtain credentials, create and update workflows, conduct ID, liveness, face-match, age-estimation, and AML checks, monitor sessions, and manage account balances. Didit’s modular workflows, integrations with tools such as Cursor and ClawHub, free core KYC offering, and automated capabilities are positioned as a way to build scalable identity systems for agent-driven applications.

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