Planning › Summary

Planning gives language model agents the ability to reason about multi-step goals before committing to action. The core ideas are:

• Task decomposition breaks goals into subtasks, identifying sequential dependencies and opportunities for parallelism. Good decompositions are complete, non-redundant, and appropriately granular.
• Planning architectures range from simple plan-and-execute to search-based tree-of-thought, trading planning cost for execution flexibility and recovery capability. PlanReAct offers the best balance for most production deployments.
• Plan execution requires managing context across steps, handling tool failures with retry logic, and monitoring progress against plan expectations. Efficient recovery depends on distinguishing recoverable failures from fixable and plan-invalidating ones.
• Replanning triggers on execution failures, observation surprises, and goal clarification, with local repair, suffix replanning, and full replanning available as escalating responses. Start with local repair and escalate only when necessary.
• Reflection-based recovery as in Reflexion stores verbal critiques of past failures to avoid repeating the same mistakes across planning attempts, implementing a form of verbal reinforcement learning without weight updates.

Planning is not a solved problem. Current systems hallucinate plans, degrade on long horizons, and pay high costs for recovery. But even imperfect planning significantly improves agent performance on complex multi-step tasks, and the field is advancing rapidly. The planning architectures covered here form the foundation for everything from software engineering agents to research assistants to autonomous workflow systems. Understanding them deeply is essential for anyone building or evaluating goal-directed AI systems.