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quiz vs the machine

Gold1380

Machine Learning

The Hierarchical Planning Agents

How agents split a big goal into subgoals and steps using planner and executor layers.

5 min read · core · beat Gold to climb

Why split planning from doing

A flat agent that picks the next action one step at a time loses sight of the goal on long tasks. Hierarchical planning separates a high level planner from a low level executor so the agent keeps a stable map of the whole job.

The two layers

  • Planner: reads the goal and produces an ordered list of subgoals, each a milestone like gather data or draft report.
  • Executor: takes one subgoal at a time and runs the concrete tool calls needed to finish it.

The planner thinks in milestones; the executor thinks in actions. Control returns to the planner after each subgoal so it can replan if something changed.

Benefits

  • Focus: the executor solves a small bounded problem.
  • Recovery: a failed subgoal triggers a replan, not a full restart.
  • Clarity: the subgoal list is a readable record of intent.

Costs and pitfalls

  • A bad initial plan misdirects all the work below it.
  • Too many layers add overhead and latency.
  • The planner needs honest feedback on whether a subgoal truly succeeded.

Hierarchical agents shine when a task is long enough that holding the whole plan in one flat loop becomes brittle.

Key idea

Hierarchical planning splits an agent into a planner that breaks a goal into subgoals and an executor that runs actions for each one, returning control to replan so long tasks stay focused and recoverable.

Check yourself

Answer to earn rating on the learn ladder.

1. What does the planner layer produce in a hierarchical agent?

2. Why does control return to the planner after each subgoal?