What is Planning?
Planning in AI agents refers to the process of decomposing high-level goals into sequences of actionable steps or subtasks that can be executed to achieve those goals. Rather than reacting immediately to each situation, planning agents think ahead, considering what needs to be done, in what order, and how different actions contribute to the ultimate objective. This forward-thinking capability enables agents to tackle complex tasks that require coordinated multi-step execution.
Planning can take various forms depending on the agent architecture. Simple linear planning creates a sequential list of steps to execute in order. Hierarchical planning breaks tasks into subtasks at multiple levels of abstraction. Dynamic planning continuously updates the plan based on execution results and changing circumstances. Some agents plan once upfront, while others engage in continuous replanning as they receive feedback and new information.
Effective planning is crucial for autonomous agent behavior. Systems like BabyAGI implement task-based planning where the agent maintains and updates a prioritized task list. ReAct-style agents interleave planning (reasoning) with action execution. More sophisticated agents might use techniques like tree search or Monte Carlo methods to explore possible action sequences. The quality of planning directly impacts an agent's ability to accomplish complex objectives efficiently, making it a central concern in agent system design.