AI orchestration is the discipline of coordinating multiple AI models, data sources, tools, and services into a coherent automated pipeline that accomplishes a goal none of them could achieve independently. An orchestration layer is responsible for routing inputs to the right model, managing state and memory across steps, handling retries and failures, enforcing rate limits, and aggregating outputs into a final result. It is distinct from a single model call in that it manages control flow—conditionals, loops, parallelism—across heterogeneous components.

Think of it like conducting an orchestra. No individual instrument produces a symphony alone; the conductor's job is to ensure each section enters at the right moment, at the right volume, and in coordination with the others. The conductor does not play the music but makes the music possible.

A minimal orchestration pattern in Python uses a router to dispatch tasks to specialized agents:

def orchestrate(user_query):
    intent = classifier_model.predict(user_query)
    if intent == "research":
        result = research_agent.run(user_query)
    elif intent == "code":
        result = coding_agent.run(user_query)
    else:
        result = general_llm.complete(user_query)
    return synthesizer_model.summarize(result)

Frameworks such as LangGraph, CrewAI, and Anthropic's Claude multi-agent patterns provide production-grade orchestration primitives.

AI orchestration is closely tied to Agentic AI (orchestration is how multi-agent systems are structured), RAG (retrieval is a common orchestration step), Mixture of Experts (routing to specialized sub-models resembles orchestration at the architecture level), Prompt Engineering (each model call requires a well-crafted prompt), and Context Window management (orchestrators must track cumulative token usage).

You will encounter AI orchestration in LLMOps documentation, enterprise AI platform marketing, and in framework guides for tools like LangChain, AutoGen, and Semantic Kernel.