// GLOSSARY -- AGENTIC AI

What is Task Decomposition?

1 min read Updated

Task decomposition is the process by which an AI agent breaks a complex goal into smaller, manageable sub-tasks that can be executed sequentially or in parallel.

WHY IT MATTERS

No agent solves complex problems in one step. Task decomposition transforms high-level goals into executable actions: check allocation, calculate trades, get quotes, execute sells, execute buys, verify results.

Effective decomposition requires planning ability — understanding dependencies, identifying parallelizable work, and handling individual step failures without losing overall progress.

For financial agents, decomposition quality directly impacts outcomes. Poor decomposition might execute trades in the wrong order or miss critical validation steps.

HOW POLICYLAYER USES THIS

When an agent decomposes a financial task into sub-steps, each sub-step involving spending needs independent policy validation. PolicyLayer evaluates every transaction in the chain — ensuring cumulative spending doesn't exceed budgets.

FREQUENTLY ASKED QUESTIONS

How do agents decompose tasks?
Typically through LLM-driven planning. The agent generates a plan of sub-tasks, then executes them sequentially — observing results and adjusting (ReAct pattern).
What if a sub-task fails?
Good frameworks handle partial failures: retry, try alternatives, or escalate to a human. For financial tasks, failed sub-tasks should trigger rollback evaluation.
Can decomposition be pre-defined?
Yes. For well-understood workflows, pre-defined task graphs are more reliable than dynamic LLM planning. Many production agents use a hybrid approach.

FURTHER READING

Enforce policies on every tool call

Intercept is the open-source MCP proxy that enforces YAML policies on AI agent tool calls. No code changes needed.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
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