Definition: AI orchestration is the coordination of models, prompts, data sources, tools and agents into a single workflow, managing the order of steps, the data passed between them, retries, errors and handoffs to people.
An orchestration layer defines the flow: retrieve context, call a model, validate the output, call a tool, branch on the result, ask a person to approve, then continue. It keeps state between steps, handles timeouts and fallbacks to another model, and records each step for observability. Frameworks such as LangChain, LangGraph and LlamaIndex, or general workflow engines, are common building blocks.
Real AI applications are rarely one model call. Orchestration turns a set of capabilities into a dependable process and is what lets agentic AI and multi-agent systems run safely at scale.
Teams decide where the model should choose the next step and where fixed code should, keep high-risk branches deterministic, and test the whole flow end to end rather than each prompt alone.
Ryz LangChain developers, LlamaIndex developers and AI pods build orchestrated workflows in your stack, working on your team.
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