Task Decomposition
Breaking a big job into small ones — which reliably helps, and reliably multiplies your failure rate.
Reviewed July 12, 2026Stable
When not to use it
- When subtasks can't be verified. Errors propagate silently and everything downstream is confidently built on them.
- Past the point of checkability. More steps is not more rigour; it's more exponent.
- Dynamically, when you know the structure. Static decomposition beats model decomposition consistently.
- On tasks the model handles whole. You've added failure modes for nothing.
Reach for something else instead
- A single well-scoped prompt — if it fits in the model's competence, don't split it.
- Map-reduce over data — independent subtasks, no compounding. The safe form.
- A hard-coded pipeline — static decomposition with the model filling steps.
- Human decomposition — a person splits, the model executes. Currently better than the model splitting.
Read more on the blog
- Neurosymbolic AI: what the symbolic half gets rightOn a structured manipulation task in early 2026, a neurosymbolic system scored 95% against 34% for a fine-tuned vision-language model, using less energy. Symbolic AI lost the last argument on cost, not on merit.
- When not to use an agentGPT-3.5 inside a structured workflow scored 95.1% on a coding benchmark. GPT-4 running free scored 67%. The structure was worth more than two generations of model improvement, and most tasks called agentic do not need an agent.
- Multi-agent AI gets worse as you add agentsOrchestrator steering accuracy falls from around 60% with three agents to about 21% with ten. Coordination is not free, and most teams reaching for multi-agent should fix their single agent first.
- Why AI agents fail: the seven failure modesGartner predicts over 40% of agentic AI projects will be canceled by 2027. The failures follow patterns, seven of them. The taxonomy: what breaks, why, which real incident proved it, and which control would have prevented it.
Further reading
- Zhou et al. (2022), Least-to-Most Prompting Enables Complex Reasoning in Large Language Models — decomposition enabling easy-to-hard generalisation.
- Khot et al. (2022), Decomposed Prompting: A Modular Approach for Solving Complex Tasks — decomposition as composable modules.
- Wu et al. (2022), AI Chains: Transparent and Controllable Human-AI Interaction via Chaining Large Language Model Prompts — the human-factors case for chaining, and its costs.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Decomposing without verification, and getting the exponent for free.
- Assuming decomposition improves reliability. It improves each step and worsens the whole.
- Letting the model decompose when you know the structure. You plan better than it does.
- Confusing independent with sequential subtasks. Only the second compounds.
At a glance
FieldAI Agents
What it buysevery step stays inside the model's competence
What it costsreliability to the power of steps
Safe formindependent subtasks (map-reduce)
Dangerous formsequential dependent steps, unverified
The real defenceverification between steps
DifficultyBeginner
Often compared with
Static vs. dynamic decomposition — you write the subtasks, or the model does. The model's version is planning, and it plans badly. Static wins consistently.
Where this sits
A destination. 30 concepts lead here, and nothing in the corpus depends on it.
16Levelsteps in
30Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstPlanning
Computed from the prerequisite graph, not assigned. How this works