A practical, production-oriented guide to building AI agents — patterns over products, anti-hype, vendor-neutral.

The Series

Part 1: The Demo Worked. Production Didn't.
Priya's refund went through on a shipped order. The model was right. The system around it wasn't. Why agent demos break the moment they meet production — and what the demo hid that production reveals.

Part 2: What Makes Something an Agent
Define what an agent actually is in engineering terms — a control loop with tools, state, and boundaries. The three primitives an agent composes (MCP for acting, RAG for knowing, Skills for following reusable procedures). The bridge from manual ReAct to native tool calling.

Part 3: How the Loop Actually Works
Coming soon. What happens turn by turn when the agent runs. State that carries across turns, stopping conditions as real decisions, and context as a finite engineering resource — not just a bigger window.

This series is actively maintained. New parts will be linked here as they publish.

Related Series in the AI in Practice Hub

MCP in Practice — Read from the beginning
The Model Context Protocol from first principles — what MCP is, why it exists, and how to build production-grade tool servers and clients.

RAG in Practice — Read from the beginning
Retrieval-augmented generation from first principles — why AI gets things wrong, what RAG fixes, and how the full pipeline works.