Task decomposition is the process of breaking a complex goal into smaller, ordered sub-tasks that can be planned, delegated, and executed independently. In AI agent systems it lets a model or orchestrator turn an open-ended request into a tractable plan, often assigning sub-tasks to specialised agents or tools. Effective decomposition improves reliability, parallelism, and the ability to recover from partial failure.
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- An agent or planner analyses a high-level objective, identifies dependencies, and produces a sequence or graph of sub-tasks that are simpler to solve and easier to verify. Sub-tasks can be dispatched to specialised agents, tools, or worker processes, enabling parallel execution and clearer error attribution. Strategies range from prompt-driven chain-of-thought planning to explicit planner-executor architectures and hierarchical task networks, with the quality of decomposition strongly shaping overall system success.