Orchestrator-workers pattern

One model plans the task, hands parts of it to worker models or subagents, and combines their results.

Unlike a fixed parallel workflow, the orchestrator decides at run time how to split the work: which files to review, which sources to research. Workers get only their slice of context, which keeps them focused and lets cheaper models do routine parts. The weak point is the merge: the orchestrator must check and reconcile results rather than paste them together.

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Related terms

Multi-agent system: Several AI agents with separate roles and contexts working on one task, usually coordinated by a lead agent or a fixed workflow.
Subagent: A separate agent run with its own fresh context, started by a main agent for one subtask.
Agentic workflow (workflow vs agent): A process where your code fixes the steps and a model does some of them, as opposed to an agent that decides its own steps.

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