Planning

What is planning in an AI agent?

Planning is the process by which an AI agent organizes future work toward a goal. A plan may identify subgoals, tasks, dependencies, constraints, tools, checkpoints, and stopping conditions.

Planning can happen before execution or be updated while the agent works. The goal stays relatively stable while the plan may change as new information appears.

How planning works

A practical planning cycle is:

Goal → Break into tasks → Order tasks → Choose actions and tools → Execute → Observe progress → Replan if needed → Finish

For example, an agent preparing a market report may first define the required sections, identify data sources, collect evidence, verify claims, draft the report, and run a final quality check. Some steps depend on earlier outputs, while independent research tasks may run in parallel.

Planning versus reasoning

Reasoning is the broader process of interpreting information and making decisions. Planning uses reasoning to organize future actions. An agent can reason about a single immediate choice without creating a full plan.

Planning also differs from task decomposition. Decomposition breaks a complex goal into smaller parts. Planning additionally orders those parts, assigns tools or owners, tracks dependencies, and defines how completion will be judged.

Planning versus workflow

A plan is created or adapted for a particular goal and current state. A workflow is a predefined sequence or graph that structures how a class of tasks runs. An agent may create a plan inside a workflow, or a workflow may execute a plan’s steps.

Plans are flexible but less predictable. Fixed workflows provide control but may handle unexpected situations poorly.

When planning helps

Planning is valuable when tasks have multiple dependencies, costly actions, limited resources, or several possible routes. Simple requests may not need an explicit plan; planning overhead can make them slower without improving quality.

Weak plans may be too vague, overly detailed, based on false assumptions, or disconnected from the tools actually available. Reliable agents update state, verify checkpoints, and replan when observations invalidate the current path.

Why it matters

Planning helps agents coordinate complex work instead of choosing every action in isolation. It makes progress, dependencies, and completion easier to inspect.

The objective is not to predict the whole future perfectly. It is to maintain a useful path that can adapt while preserving the goal and constraints. See [Planning in AI Agents](/planning-in-ai-agents/) for the full planning and replanning loop.