Agentic Planning & Task Decomposition
Agentic planning and task decomposition break a goal into actions or subgoals that an agent can execute and revise. The skill includes identifying dependencies, selecting the right level of detail and updating the plan when observations invalidate assumptions, while preserving a clear criterion for completion.
What it is
A planner represents how intermediate results could lead to the desired outcome. A language-model plan may be a sequence of instructions, a dependency graph or a hierarchy of tasks, while execution produces observations that can trigger replanning. Planner–executor separation makes the proposed steps explicit before another component performs them. Plan-and-Solve is a prompting example of decomposing before solving; formal planning systems may instead use specified actions and preconditions. These are related approaches with different guarantees. A verbal plan is a hypothesis about the task, and its feasibility depends on available tools, resources and the current environment.
What the work involves
The practitioner defines the goal and available actions, then chooses a planning representation suited to dependencies and uncertainty. Each subtask should have an observable result and a reason for being necessary. The execution policy needs rules for failed preconditions, missing information and plan revision. A useful deliverable is an inspectable plan linked to actual task results. Evaluation checks whether decomposition omits required work, creates unnecessary steps or prevents adaptation after discovering that the original route cannot succeed.
Illustrative example
An agent is asked to prepare a migration checklist for a service. It decomposes the task into inventorying dependencies, checking compatibility, identifying data-transfer requirements and defining rollback steps. When it discovers a dependency that cannot run on the target runtime, it adds a compatibility decision before deployment preparation. A reviewer inspects that dependency in the plan and verifies that subsequent tasks wait for the decision instead of treating the initial sequence as immutable.
Limits and common mistakes
A detailed plan can create false confidence when its assumptions remain untested. Over-decomposition wastes time, while broad subtasks hide important dependencies. Plans may also drift as intermediate outputs redefine the goal. Quality depends on executable steps, clear completion evidence and sensible replanning triggers. Formal guarantees available in a specified planning model do not automatically transfer to natural-language plans generated by an LLM, especially when tools expose uncertain or changing external behavior.
Prerequisites
- hardAI Agent Design
Planning is the core of an agent's control loop.
Sources and further reading
- Plan-and-Solve Prompting
Studies a two-stage prompting method that first decomposes a problem and then carries out the resulting plan.
Last updated: 2026-10-10