Planning and Task Decomposition
Agentic AI starts with a plan. Rather than acting on a raw goal, the system breaks it into executable steps first, which keeps long work from drifting.
Why Plan First
Without a plan, an AI decides each next action from whatever is directly in front of it. Small early errors compound, and the direction has shifted by the time anyone notices.
Producing the plan up front gives four benefits.
| Benefit | Description |
|---|---|
| Early correction | A person can check and adjust direction before work begins |
| Visible progress | What is done and what remains stays clear |
| Partial re-runs | Only the failed step needs redoing |
| Completion checks | Each step can carry its own completion condition |
A Good Unit of Decomposition
Finer is not automatically better. Two criteria apply.
- One step, one purpose — “research, implement, and verify” is three steps
- Completion is checkable — not “research thoroughly” but “identify the target files”
# Too coarse
1. Write the article
# Appropriately decomposed
1. Identify the primary sources to reference
2. Draft the structure
3. Write the body
4. Check terminology and internal linksSplitting too finely has the opposite failure: handoffs multiply and the overall intent thins out. A useful gauge is whether a person can read the list and follow the flow.
Plans Change
A plan is not fixed once written. Revising it when an assumption breaks is the correct behavior.
graph LR
P["Produce a plan"] --> E["Execute a step"]
E --> C["Do the assumptions still hold"]
C -->|Yes| E
C -->|No| PExecution results supply the evidence for that judgment. Having a path back to the plan when results differ from expectations is what leads into self-correction.
Summary
- Planning breaks a goal into executable steps before work starts
- Decompose by “one purpose per step” and “completion can be checked”
- Over-splitting obscures the overall intent
- Build in a route back to the plan when assumptions break