Planning in AI
The process by which an AI agent determines a sequence of actions to achieve a goal.
Full Definition
Planning in AI agents is the process of decomposing a high-level goal into a structured sequence of sub-tasks, selecting appropriate tools or strategies for each step, and ordering actions to satisfy dependencies and constraints. Planning can be done upfront (creating a full plan before acting) or reactively (adapting the plan after each action's result). LLM-based planning leverages the model's reasoning capabilities to generate task decompositions in natural language. Key challenges include plan grounding (ensuring actions are feasible), plan revision (recovering from unexpected results), and long-horizon planning (maintaining coherence over many steps). Tree of Thoughts, ReAct, and dedicated planning frameworks (like Plan-and-Execute) are common implementations.
Examples
An agent given 'write a research report on climate change solutions' decomposing the task into: identify sub-topics → search each → extract key points → draft sections → edit → format.
A Plan-and-Execute agent creating an upfront 10-step plan for booking a trip, then executing each step with tools, revising the plan when a flight is unavailable.
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