Two colleagues reviewing documents at an office desk, illustrating collaborative task planning and hierarchical breakdown — Photo by Vitaly Gariev on Unsplash

What Is Task Decomposition in AI Agents?

Q: How do AI agents decompose tasks automatically?

Agents use chain-of-thought reasoning or dedicated planning prompts to generate decomposed plans. The model is given the overall goal and asked to identify the subtasks required, their ordering, and any dependencies between them. Frameworks like LangGraph and CrewAI provide structured patterns for executing these decomposed plans.

Q: When should subtasks run in parallel versus sequentially?

Subtasks can run in parallel when they are independent — one subtask's result is not required as input for another. They must run sequentially when there is a dependency: for example, you cannot summarize results before you have gathered them. Identifying parallelizable subtasks is one of the most impactful optimizations in agent workflow design because it directly reduces total execution time.

Quick Definition#

Task decomposition is the process of breaking a complex, high-level goal into a set of smaller, concrete subtasks that can be executed individually. Rather than attempting a complex task in a single step, an agent with effective decomposition plans out each component of the work, identifies dependencies between steps, and produces an ordered execution plan.

Effective task decomposition is foundational to reliable agent performance on complex workflows. Without it, agents attempt too much in single steps, lose track of progress, and produce incomplete or inconsistent results. For related concepts, see Agent Planning and The Agent Loop. Browse the full AI Agents Glossary for more related terms.

Why Task Decomposition Matters#

Complex tasks have multiple moving parts. Writing a competitive analysis report requires: identifying competitors, gathering product information, comparing features, analyzing pricing, and synthesizing findings. A customer onboarding workflow requires: validating account data, creating records in multiple systems, sending welcome communications, and scheduling follow-up tasks.

Attempting these as single monolithic operations produces degraded results because:

The model must manage too many concerns simultaneously
Errors in one aspect contaminate the entire output
Progress is not measurable until the end
Failures are difficult to diagnose and recover from

Decomposition addresses all of these by creating a structured execution plan with clear units of progress and explicit handoff points.

For deployment context, read AI Agent Examples in Business to see how decomposition works in real workflows.

How Agents Decompose Tasks#

Prompted decomposition#

The simplest approach: instruct the agent to produce a decomposed plan before executing anything.

A planning prompt might look like: "Before beginning this task, list every subtask required to complete it. For each subtask, specify: what it will do, what tool or action it will use, what its inputs are, and what its expected output is."

This produces an explicit, auditable plan that can be reviewed before execution begins.

Chain-of-thought decomposition#

The agent uses Chain-of-Thought Reasoning to work through the problem structure and identify natural decomposition points. The reasoning process itself surfaces the subtasks and their relationships.

Hierarchical decomposition#

For very complex tasks, decomposition can happen in layers:

High-level phases: Major stages of the work (research → analysis → synthesis → output)
Tasks within phases: Specific operations within each phase
Subtasks: Atomic actions that map to single tool calls or operations

This hierarchy makes it easier to track progress, identify blockers, and recover from failures at the appropriate level.

Parallel vs. Sequential Subtask Execution#

One of the highest-impact decisions in decomposition design is whether subtasks should run in parallel or sequentially.

Sequential execution#

Subtasks must run sequentially when there is a dependency: the output of one subtask is required as input for the next.

Example sequential chain:

Search for competitor pricing data → produces: price list
Extract prices from search results → requires: price list, produces: structured price data
Compare prices against baseline → requires: structured price data, produces: comparison table

These three steps cannot be reordered or parallelized because each requires the previous step's output.

Parallel execution#

Subtasks that are independent can run simultaneously, reducing total execution time proportionally to the degree of parallelism.

Example parallel opportunity:

Search for Competitor A's pricing (independent)
Search for Competitor B's pricing (independent)
Search for Competitor C's pricing (independent)

All three searches can run at the same time. Total time is determined by the slowest search, not the sum of all three.

Identifying parallelism opportunities#

The key question for each pair of subtasks: does either require the other's output? If no, they can potentially run in parallel. Build a dependency graph from the subtask list and execute all subtasks at the same dependency level simultaneously.

For multi-agent workflows where different agents can execute parallel subtasks independently, see Multi-Agent Systems.

Hierarchical Planning#

Hierarchical task decomposition creates a tree structure where high-level goals are decomposed into phases, phases into tasks, and tasks into atomic operations.

This approach has several advantages:

Progress tracking: High-level phase completion provides meaningful milestones without needing to track every atomic operation.

Error isolation: Failures occur at the task or atomic level and are easier to diagnose and recover from when the hierarchy is explicit.

Human review points: In Human-in-the-Loop AI workflows, humans can review at the phase level before approving each phase's execution, rather than reviewing every individual operation.

Parallel coordination: Different phases or independent tasks within a phase can be assigned to different agents running in parallel, which is the core pattern in AI Agent Orchestration.

Common Decomposition Failures#

Over-decomposition#

Breaking tasks into too many tiny subtasks creates coordination overhead without adding value. If a subtask can only be completed with one trivial action, grouping it with a related subtask usually produces better results.

Under-decomposition#

Leaving subtasks too large leads back to the original problem: the agent must manage too many concerns in a single step. Any subtask that requires multiple distinct operations should be decomposed further.

Missing dependencies#

The agent generates a plan that looks reasonable but attempts to use an output before it has been produced. Explicit dependency mapping during decomposition prevents this.

Brittle sequential plans#

A plan with many sequential dependencies is fragile — any failure blocks all subsequent steps. Where possible, identify opportunities to restructure the plan to run more steps in parallel or to make later steps less dependent on specific intermediate results.

Failure Recovery in Decomposed Workflows#

Decomposed workflows benefit from structured failure recovery:

Step-level retry: When a single subtask fails, retry that step with a modified approach before aborting the entire workflow.

Partial completion recovery: If the workflow can be resumed from the point of failure rather than restarted from the beginning, define checkpoints at key subtask boundaries and save state at each checkpoint.

Alternative path planning: When a subtask fails in a way that suggests the original approach is wrong, trigger replanning from the current state rather than retrying the same approach.

Escalation threshold: After N retry attempts at a given step, escalate to a human review rather than continuing to consume compute on a stuck workflow.

Implementation Checklist#

Define decomposition explicitly in planning prompts before any execution begins.
Map dependencies between subtasks before scheduling execution order.
Identify subtasks that can run in parallel and implement parallel execution.
Define success criteria for each subtask, not just the overall goal.
Add checkpointing at phase boundaries for long-running workflows.
Implement step-level retry before aborting full workflows.
Use Agent Evaluation to measure decomposition quality through task completion rates.

Frequently Asked Questions#