Agent Planning and Task Decomposition

Planning is the brain of an AI agent. While tool use and reasoning answer "what can we do?" and "what should we do?", planning answers "what sequence of actions will accomplish our goal?" This series teaches you how to decompose complex objectives into manageable tasks, structure those tasks into dependency graphs, build reliable plan-and-execute loops, and handle the real-world messiness of long-horizon planning—incomplete information, execution failures, and environments that change mid-plan.

You'll learn why naive one-step prompting fails for multi-step problems (it does—token limits and reasoning depth break down after 3–4 hops), how ReAct reasoning-and-acting loops beat straight-line execution, and how production systems use dynamic replanning, state tracking, and reflection to recover from surprises. By the end, you'll be able to architect agents that can plan a week-long project, adapt when a dependency fails, and know when to ask for human input instead of hallucinating recovery steps.

This series moves from goal decomposition fundamentals through task graphs and plan-and-execute patterns, then into reflection loops, dynamic replanning, and the engineering practices that ship planning-based agents at scale. Each article includes concrete code, worked examples, and references to how organizations like Anthropic, DeepSeek, and others are shipping agentic systems in production today.

Articles in this series​

Articles in this series