What is AI Orchestration?
AI orchestration is the coordination of multiple AI models, tools, data sources, and processing steps into unified workflows. An orchestration layer manages the flow of information between components — deciding which model to call, when to use tools, how to handle errors, and how to combine results into a coherent output.
Why It Matters
Real-world AI applications rarely use a single model in isolation. A customer support system might use an intent classifier, a RAG retrieval system, an LLM for response generation, and a sentiment analyzer for quality control — all coordinated by an orchestration layer. As AI systems grow more complex and agentic, orchestration becomes the critical middle layer.
How It Works
Core orchestration patterns:
1. Sequential chains:
- Output of one step feeds into the next
- Example: retrieve documents → summarize → generate email
- Frameworks: LangChain chains, LlamaIndex pipelines
2. Routing / branching:
- Classify the input and route to specialized handlers
- Example: customer query → intent classifier → route to billing/technical/general agent
- Each branch may use different models or tools
3. Parallel execution: