AI Agents & MCP

Agents that work, not just demo

AI agents are the most exciting — and most over-promised — area in applied AI right now. An agent that calls tools and takes actions can be genuinely powerful, but reliability is the whole game: guardrails, sensible tool use, and graceful handling of the many ways things go wrong.

This section covers building agents that hold up: when an agentic workflow actually beats a single well-designed LLM call, how to add guardrails and tool use without creating a black box, and the Model Context Protocol (MCP) — a growing standard for exposing tools and data to models.

Topics

• Building reliable AI agents: guardrails, tool use, and failure handling
• Agentic workflows vs. single LLM calls: when each wins
• Model Context Protocol (MCP) for engineering teams