Under the Hood of AI Agents: A Technical Guide to the Next Frontier of Gen AI

Why It Matters

The practical result is more powerful, always-on AI assistants but also new operational demands and security/authorization challenges for businesses that must invest in tooling, standards and governance to scale agents safely.

Summary

Agentic AI—LLM-based systems that autonomously run tools in a thought-action-observation loop—are rapidly moving from chat prototypes to production, enabling tasks from booking travel to coding. Key infrastructure components include agent development frameworks, cloud model hosting, tool-call protocols (notably the year‑old Model Context Protocol), short- and long-term memory, tracing, and session-based isolation using microVMs (e.g., Firecracker) to securely run LLM sessions. The practical result is more powerful, always-on AI assistants but also new operational demands and security/authorization challenges for businesses that must invest in tooling, standards and governance to scale agents safely.