Task Decomposition
The process where an agent breaks a complex goal into smaller, manageable subtasks that can be executed sequentially or in parallel. Effective task decomposition is fundamental to agent reliability on multi-step problems.
Task decomposition is how agents handle problems too complex for a single step. Given a goal like "create a competitive analysis report," the agent decomposes it into subtasks: identify competitors, gather pricing data, analyze feature comparisons, research market positioning, synthesize findings, and format the report. Each subtask becomes a tractable unit of work with clear inputs and outputs.
For growth engineering teams, task decomposition quality determines agent usefulness on real-world problems. Poor decomposition leads to agents that skip steps, produce incomplete results, or get stuck in loops. Good decomposition creates clear checkpoints where you can validate progress and recover from failures. Design your agent systems with explicit decomposition steps that are logged and reviewable. When building custom agents, consider providing decomposition templates for common task types rather than relying entirely on the model's ability to decompose novel problems from scratch.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.