AI Agents: The Definitive Guide

Chapter 2. Architectures and Patterns: Planning, Reactivity, and Multi-Agent-Systems

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 2nd chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

You know by now that AI agents are not magic. They are engineered systems whose power comes from the strategic configuration of their architecture. By combining reasoning loops, tools, stateful design, and adaptive control, LLMs can move beyond static prompts and evolve into MAS that plan, react, and reflect in real time. This chapter explores the architectures that make this possible, from structured reasoning methods such as chain of thought, tree of thought, and ReAct to architectural paradigms like supervisor and hierarchical agents. Along the way you will gain a deeper understanding of reasoning flows and see how human-in-the-loop mechanisms including approval gates, corrections, and interruptions keep these systems reliable, aligned, and secure.

The importance of an agent’s architecture becomes obvious when you compare how a simple single-step agent handles a task versus how a multi-step agent does. A ...