Planning (AI Agents)

Planning in AI agents refers to the capability of breaking down a complex goal into manageable sub-tasks, ordering them logically, and determining what resources and actions each step requires. It is one of the core abilities that separates a simple chatbot from a genuine AI agent.

Why planning matters

Without planning, an AI model responds to each message in isolation. It can answer questions and generate text, but it cannot manage a multi-step project. Planning enables an agent to receive a goal like "prepare a competitive analysis report" and independently determine that it needs to identify competitors, gather data on each, analyse strengths and weaknesses, and compile findings into a structured document.

How AI agents plan

Current AI agents use several planning approaches:

• Chain-of-thought decomposition: The model reasons through the task step by step, writing out its plan before executing. This leverages the model's language capabilities to structure work.
• Recursive decomposition: The agent breaks a goal into sub-goals, then breaks each sub-goal into smaller tasks, continuing until each task is simple enough to execute directly.
• Template-based planning: The agent matches the goal to a known workflow template and adapts it to the specific situation. This is more reliable but less flexible.
• Iterative planning: The agent creates an initial plan, executes the first steps, evaluates results, and revises the remaining plan based on what it learned.

Planning challenges

• Over-planning: Creating unnecessarily detailed plans that waste time and tokens on sub-tasks that could be handled on the fly.
• Brittle plans: Plans that fail entirely when one step produces unexpected results, rather than adapting gracefully.
• Goal misinterpretation: The agent decomposes the wrong goal because it misunderstood the user's intent.
• Resource estimation: Accurately predicting how many steps, tools, and API calls a task will require.

Best practices

Effective agent planning combines high-level structure with flexibility. The agent should create a clear plan, share it with the user for validation before execution, and be prepared to revise as circumstances change. Human-in-the-loop checkpoints at key decision points significantly improve reliability.