Orchestration is the control layer that decides what runs when in an AI pipeline — which agent gets called, with what context, in what order, and how results get passed along. In single-agent systems it's the loop logic. In multi-agent systems it's the router or planner that delegates.
Frameworks like LangGraph, CrewAI, and AutoGen are orchestration tools. They give you primitives: nodes (steps), edges (transitions), state management, and error handling. The trade-off: they abstract away the control flow, which is great for iteration speed but makes debugging harder when something goes wrong.
Many teams build their own thin orchestration layer rather than adopting a framework — a Python function that chains a few async model calls is often simpler and more debuggable than a full framework for a three-step pipeline.
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