Authenticating AI Agents (fusionauth.io)

AI agents — LLM-driven workflows that call APIs and take actions on behalf of users or other agents — are becoming a third interaction layer on the web alongside humans and traditional software. The post argues authentication and authorization must evolve accordingly, covering common protocols (A2A, Anthropic’s Model Context Protocol/MCP, and direct API access), deployment patterns (local env vars for dev vs. OAuth 2.1 for production), and how agents differ from simple chatbots by being autonomous, multi-step, and non-deterministic. The technical implications are clear: authentication alone isn’t enough — authorization, auditing, and operational controls are critical. A2A is “authentication neutral” and advertises requirements via Agent Cards; MCP standardizes tool access and favors OAuth for remote deployments. To mitigate the “lethal trifecta” (private data access + external comms + untrusted input) apply least privilege: scoped credentials (never full user creds), distinct agent identities in logs, and RBAC/ABAC/PBAC/ReBAC as appropriate. Also implement rate limits, detailed agent-specific auditing, monitoring, retry/fallback logic, and clear decision workflows (who/which protocol/what permissions). Organizations should inventory agent use, map data access, document auth models, and integrate agent access into existing security posture before scaling agent-enabled features.