Deep dives on AI agent governance, MCP security, compliance, and enterprise agentic architecture.
OAuth 2.1 consolidates OAuth 2.0 and its security best practices: mandatory PKCE, no implicit grant, exact redirect matching. Plus what it means for AI agents.
OAuth token exchange (RFC 8693) swaps a broad token for a narrow, audience-bound one. How delegation, impersonation, and the act claim work for AI agents.
Human in the loop AI puts a person on the calls an agent should not make alone. Which actions need approval, how to design the checkpoint, what auditors want.
What agent authorization is, which model to use, how delegated authority travels in a token, and where the decision belongs so a prompt cannot move it.
What ephemeral credentials are, how they are issued, how long they should live, and how to move AI agents off static secrets without breaking production.
Step-up authentication asks for stronger proof only when an action's risk earns it. How it works, when to trigger it, and how it extends to AI agents.
AI security protects AI models, data, and the actions agents take. Learn the definition, the real AI security issues, standards, and controls that work.
AI agent architecture explained: the six layers, the agent loop, five design patterns, per-layer failure modes, and the accountability layer most miss.