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Plain-language AI glossary

Term 54InfrastructureMeaning / context / connections

Infrastructure / Definition

AI Orchestration

The coordination of multiple AI models, tools, and data sources in a unified pipeline to accomplish complex tasks that no single model can handle alone.

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MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How AI Orchestration works.

AI orchestration is how you build real AI products: by combining multiple components rather than relying on a single LLM call. A typical orchestrated pipeline might: (1) use a small, fast model to classify the user's intent, (2) route to a specialized model or tool based on intent, (3) retrieve relevant context from a vector database, (4) call a powerful model with the enriched context, (5) validate the output with a guardrail model, (6) return the result.

Orchestration frameworks include LangChain (Python/JS, general purpose), LlamaIndex (data-focused), Semantic Kernel (Microsoft/.NET), and custom pipelines. The key decisions are: which model for each step (use cheap models for classification, expensive ones for generation), how to handle failures (retry, fallback, escalate), and how to manage state across steps.

For production systems, orchestration also means: observability (trace each step to debug failures), caching (avoid redundant LLM calls), rate limiting (stay within API quotas), and cost tracking (know exactly what each request costs across all models and services used).

02 / Practical uses

Where it helps.

  1. 01Multi-model AI applications
  2. 02Intent classification and routing
  3. 03Complex document processing pipelines
  4. 04AI-powered search systems
  5. 05Enterprise AI platforms
03 / Keep exploring
AI AgentAn AI system that can autonomously plan, reason, use tools, and take actions to accomplish goals with minimal human intervention.Agentic WorkflowA multi-step AI process where an LLM autonomously plans, executes, and iterates on tasks using tools and feedback loops.AI PipelineA sequence of data processing and AI inference steps that transforms raw input into a useful output, typically involving preprocessing, model inference, and post-processing.Function Calling (Tool Use)An AI capability where the model can decide to invoke external functions or APIs based on the conversation context.