How do you implement planning and task decomposition in AI agents?

Planning enables agents to break complex tasks into manageable steps before execution. Planning approaches: (1) Sequential planning: agent generates a complete plan upfront, then executes steps in order. Works well for predictable tasks. (2) Adaptive planning: agent plans a few steps ahead, executes, observes results, and replans. Better for uncertain environments. (3) Hierarchical planning: decompose tasks into high-level goals and low-level actions, plan at both levels, enabling both strategic thinking and tactical execution. Implementation: prompt the LLM with the task and available tools, request a structured plan (numbered steps with expected outcomes), execute each step while tracking progress, replan if step results deviate from expectations. Plan-and-execute pattern: planner agent creates the overall plan, executor agent carries out individual steps, and replanner adjusts the plan based on execution results and new information. Task decomposition techniques: prompt-based decomposition (ask the LLM to break the task into subtasks), recursive decomposition (break subtasks further until each is atomic), and dependency analysis (identify which subtasks depend on others for parallel execution). Plan representation: structured formats (JSON with steps, dependencies, expected outcomes), DAG for parallel execution planning, and decision trees for conditional planning. Evaluation: plan quality (does it cover all necessary steps, are dependencies correct), execution efficiency (minimum steps to completion), adaptability (how well does the agent replan after unexpected results), and completion rate (percentage of plans successfully executed to completion). Advanced: Monte Carlo Tree Search for exploring plan alternatives, and reinforcement learning for improving planning through experience.