AI Agents: The Definitive Guide

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

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 single-step agent receives the request, plans everything internally, executes in one go, and returns the result without ever revisiting intermediate steps. A multi-step agent, in contrast, alternates between reasoning and action in an iterative loop, adapting its plan based on partial results or new observations as it works. This flexibility is what allows agents to refine outputs dynamically and cope with uncertainty or feedback from their environment. The contrast is illustrated in Figure 2-1 ...