AI Orchestration
Coordinating multiple AI models, agents, tools, and steps into a managed workflow so they work together toward a larger goal.
AI orchestration is the coordination of multiple AI components — models, agents, tools, data sources, and processing steps — into a managed workflow that accomplishes a larger goal than any single call could. Rather than treating a language-model request as a one-off, orchestration sequences and routes work: deciding which model or agent handles a step, passing outputs from one stage to the next, calling external tools, handling errors and retries, and enforcing checkpoints such as human approval.
Orchestration matters because real business tasks rarely fit into a single prompt. A request may require gathering data from several systems, running it through one or more models, validating results, and taking an action. An orchestration layer manages this control flow, including branching logic, parallel steps, and failure recovery, and often maintains state and an audit trail of what each component did and why.
Orchestration logic can be written as ordinary code, defined as a workflow in a dedicated orchestration engine, or handed to a coordinating agent that decides the next step while the work runs. Many systems mix the approaches: fixed steps where the process is known in advance, and an agent's judgment only where it is not.
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