A practical, production-oriented guide to AI agents — from why demos break in production to the architecture choices, control surfaces, and failure modes that make them hold up. Patterns over products. No tool hype.
Examples use a fictional company, TechNova, as a running thread.
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 Control Loop Actually Works
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.
Part 4: Five Agent Patterns and the Control Surfaces That Make Them Safe
The five shapes an agent loop takes — prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer — and the nine control surfaces that decide whether each shape is safe to ship.
Part 5: Workflow, Agent, or Single LLM Call — How to Decide
Five practical architectures ordered from lowest cost to most flexible, and the one question that chooses among them: who decides the next step. Why hybrid is the steady-state shape for most production systems, and the warning signs that you reached too high on the ladder.
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.
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