Planning (AI Agent Context)

Why It Matters

Planning is what transforms an LLM from a question-answerer into a goal-achiever. Without planning, agents can only react to immediate prompts. With planning, they can tackle complex tasks that require multiple steps, coordinate resources, and adapt when things go wrong.

Real-world tasks rarely have single-step solutions. “Book a trip to Paris” requires searching flights, checking hotel availability, considering timing constraints, and making reservations in the right order. A planning-capable agent breaks this down, sequences the steps logically, and handles dependencies.

For AI engineers, planning capability is what makes agents useful for substantial work. A coding agent that plans reads the codebase, designs an approach, implements changes, and tests results, rather than randomly editing files. Planning is the difference between an agent that helps and one that creates messes.

Implementation Basics

Planning in AI agents involves several components:

1. Goal Understanding The agent must parse what the user actually wants to achieve. This often requires clarifying questions, interpreting ambiguous requests, and identifying success criteria.

2. Task Decomposition Break complex goals into smaller, manageable subtasks. Each subtask should be something the agent can accomplish with its available tools. This is often done through chain-of-thought reasoning.

3. Action Sequencing Determine the order of operations. Which tasks depend on others? What can run in parallel? What needs to happen first to enable later steps?

4. Resource Planning Consider constraints: time, cost, available tools, context window limits. A plan that requires 100 API calls might need to be restructured for efficiency.

5. Plan Execution Work through the plan step by step. Monitor progress. Handle errors and unexpected results.

6. Plan Adaptation When execution doesn’t go as expected, revise the plan. Good agents recognize when their approach isn’t working and try alternatives.

Planning Patterns

• Upfront Planning: Generate a complete plan before starting execution. Good when the task structure is clear.
• Incremental Planning: Plan one or few steps ahead, then re-plan based on results. Good for uncertain environments.
• Hierarchical Planning: Create a high-level plan, then expand each step into detailed sub-plans. Manages complexity for large tasks.

Practical Considerations

• Validate plans before execution. Check that required tools exist and dependencies are satisfiable.
• Set plan complexity limits. Overly elaborate plans often fail in practice.
• Log plans and their modifications. Essential for debugging why agents take unexpected actions.
• Let users view and approve plans before execution for critical tasks.

Planning quality varies significantly across models. Evaluate planning capability specifically. Don’t assume a model that generates good text can also generate good plans.