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Core Concepts

Planning

Planning is the step where an agent turns a goal into an ordered set of smaller steps before — or while — it starts acting, so it isn't deciding what to do purely one action at a time with no larger structure.

GoalBreak into stepsOrder & scheduleExecute
Planning turns a goal into ordered, schedulable steps before execution.

Left to decide one action at a time with no larger structure, an agent can wander — taking a locally reasonable next step that doesn't actually lead anywhere useful, or redoing work because it never had an overall shape for the task. Planning is the step, or set of steps, where an agent works out that overall shape: what the goal actually breaks down into, what order the pieces need to happen in, and what depends on what, before or while it starts executing.

Planning can happen once, up front, producing a fixed list of steps the agent then works through — or it can be revisited continuously, with the agent replanning as it learns things partway through that change what the remaining steps should be. Both approaches are common; a fixed up-front plan is simpler to reason about and show to a user for review, while continuous replanning adapts better to tasks where a lot can't be known until the agent has already started.

Planning is closely tied to task-decomposition (breaking a goal into steps is most of what planning does) and sits as a more deliberate version of the 'plan' step inside the general agent-loop — for a simple task, planning might just be picking the next single action, while for a complex one it looks more like the explicit multi-step process this entry describes.

How it works

The diagram's four nodes are a common shape: starting from a goal, an agent (or a planning-focused component) breaks it into steps — smaller, more concrete pieces of work — then works out how those steps should be ordered and scheduled, accounting for dependencies (step 3 needs step 1's output) and what can run in parallel, and finally executes the resulting plan, often looping back to replan if execution reveals the plan doesn't fit reality.

Example

Given the goal 'migrate this service to a new database,' a planning step might break it into: audit current schema usage, write a migration script, run it against a staging copy, verify data integrity, then cut over production — recognizing that verification has to happen before cutover, but the audit and writing the migration script could partly happen in parallel.

How it differs

Planning is sometimes used loosely as a synonym for task-decomposition, but decomposition is really the sub-step of breaking a goal into pieces; planning is the broader activity that also includes ordering those pieces, handling dependencies between them, and deciding when to revisit the plan as new information comes in.

Common misconceptions

Often assumed: An agent always plans every task before doing anything.
Actually: Many simple tasks don't need an explicit planning phase at all — the agent loop's single 'decide next action' step is enough; explicit multi-step planning tends to show up mainly for larger or more dependency-heavy tasks.
Often assumed: A plan, once made, is followed exactly step by step.
Actually: Many agents replan partway through when execution reveals something the original plan didn't account for, rather than rigidly sticking to the first plan produced.

FAQ

What is planning in an AI agent?
It's the step where an agent breaks a goal into an ordered set of smaller steps, accounting for dependencies, before or while it starts acting on them.
Is planning the same as task decomposition?
They overlap but aren't identical — decomposition is the sub-step of breaking a goal into pieces, while planning also covers ordering those pieces and revisiting the plan as needed.
Do agents replan if something changes mid-task?
Many do — continuous replanning is common for tasks where information that affects the remaining steps only becomes available once the agent has already started.

Last checked: 2026-08-28

Related terms

Task Decomposition

Task decomposition is the process of breaking one large or ambiguous task into smaller, more concrete subtasks that can each be executed and checked individually.

Agent Loop

The agent loop is the repeating perceive-plan-act-observe cycle by which an AI agent turns a goal into a sequence of actions, checking each result before deciding what to do next.

Chain-of-Thought (CoT)

Chain-of-thought is a prompting technique where a model is asked to write out its intermediate reasoning steps before giving a final answer, rather than producing the answer directly.