Orchestration
Orchestration is the systematic coordination of multiple LLM calls, external tool executions, and conditional logic flows to complete complex, multi-step tasks.
What it is
An orchestration layer sits between your application code and one or more LLMs to manage state, retry logic, and data flow between steps. It typically handles dependencies where the output of one model call is parsed, validated, and injected into the prompt of the subsequent step. When managing complex workflows, these systems often maintain a shared execution context across 5 to 20 sequential calls, ensuring that token usage and latency remain within acceptable bounds for the user experience.
Why it matters
Without formal orchestration, your application code becomes a fragile mess of nested conditional statements and manual state management. Ignoring this leads to unobservable failure modes, such as silent parsing errors or cascading latency spikes when a single sub-step fails. Effective orchestration lets you decouple the business logic from the specific prompt engineering, making your AI features easier to test, monitor for cost, and debug in production.
In practice
You implement orchestration using frameworks like LangGraph or temporal workflows to define a directed acyclic graph of execution steps. You will observe this through the audit logs or tracing tools, where you monitor the status of individual nodes and transition times between model invocations. Adjusting the flow involves modifying the state schema or changing the routing logic that determines which tool or model to call next.
The tradeoff
The primary tradeoff is between developer control and system complexity: highly structured orchestrations prevent hallucinations but increase latency and brittle dependency chains.