Planning › Planning

Language model agents can answer a question, call a tool, and synthesize a result in a single step. But what happens when the task requires ten steps, some of which depend on the outcomes of earlier ones, and the path to success is not obvious at the start? Single-step reasoning breaks down. The agent commits to an action too early, gets stuck when something unexpected happens, or loses track of the overall goal in a tangle of sub-problems.

Planning is the capacity to reason about sequences of actions before taking them. A planning agent does not immediately act. It decomposes the goal into subtasks, estimates what each subtask requires, identifies dependencies between them, and only then begins execution. When execution goes wrong, a good planner revises the plan rather than abandoning the goal entirely. This is not a minor engineering detail. It is the difference between an agent that can be delegated a complex goal and one that requires constant supervision to stay on track.

This chapter covers the core ideas behind LLM-based planning: what planning means in the context of language model agents, how task decomposition works, the main planning architectures available today, how plans get executed and monitored, and how agents recover when plans fail. We also implement a working planner from scratch that demonstrates these ideas in code. Building on the ReAct pattern and agent architectures covered in earlier parts of this book, planning adds a layer of deliberate foresight that separates simple tool-calling agents from goal-directed systems capable of multi-step autonomous work.