LLM orchestration
What is LLM orchestration?
LLM orchestration is the coordination layer that decides which large language model call, tool, or prompt step runs next in a multi-step task — passing state and conversation memory between steps, routing a request to the right specialized model, and sequencing tool calls so a workflow completes reliably instead of relying on one single, one-shot prompt. It's the infrastructure layer an AI agent's tool-calling loop runs on top of, and what lets a workflow chain several models or tools together as one coherent task.
More detail
Orchestration frameworks typically model a task as a graph or chain of steps — call a model, decide whether to call a tool, call another model or the same one again, then decide the next step — rather than a single fixed prompt-and-response exchange, so a request can loop, branch, or run steps in parallel until the task is done.
Orchestration also covers model routing — sending a simpler step to a smaller, cheaper model and only escalating a harder step to a larger, more capable one — plus retry and fallback logic when a model call fails or returns a low-confidence result, so a single provider hiccup doesn't silently break the whole workflow.
On Orbit, LLM orchestration is the layer underneath the AI agent framework that powers voice and chat agents — it sequences tool calls, carries memory and context across a call or thread, and hands the conversation off to a human agent mid-task when needed, across phone, SMS, WhatsApp, and web chat.
Frequently asked
- What's the difference between LLM orchestration and an AI agent?
- An AI agent is a single system that plans steps and calls tools to complete a task. LLM orchestration is the broader coordination layer an agent — or a pipeline of several agents and models — runs on top of, deciding call order, passing state between steps, and routing each step to the right model or tool.
- Does LLM orchestration always involve more than one model?
- No — a single model can be orchestrated through several sequential or looped calls to complete one task. Orchestration also commonly routes between multiple different models, sending simpler steps to a smaller model and reserving a larger one for the steps that need it.
See also
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