Introduction

The promise of autonomous agents has been with us since the early days of artificial intelligence research. Yet only in the last few years have the pieces finally aligned: large language models with broad world knowledge, robust tool-use capabilities, and standardized protocols for connecting agents to the systems they need to operate.

This guide is for practitioners who have moved past the demo phase. You have a working ReAct loop, you’ve integrated an LLM API, and now you’re trying to make the thing reliable enough to run unattended in production. That gap — from demo to production — is where most agent projects die. This book is about crossing it.

The difference between a prototype and a product is roughly three orders of magnitude in complexity.

— Field Note

We will not be building toy examples. Every pattern in this guide has been extracted from real systems processing real data at scale — clinical notes, infrastructure logs, codebases, customer support tickets. The failures described here are ones we have lived through. The solutions are ones we have shipped.

Who This Is For

You should read this book if you:

• Have built at least one working LLM agent and felt the pain of making it reliable
• Need to integrate agents into existing systems with real data and real users
• Care about observability, evaluation, and graceful degradation
• Are skeptical of hype and want field-tested patterns

You should probably skip this book if you are looking for:

• An introduction to LLMs or prompt engineering
• A survey of agent architectures without implementation details
• Framework-specific tutorials (we use raw Python, not LangChain)