Orchestration
The coordination of multiple agent calls, tools, and models to accomplish a complex, multi-step task — including managing state, routing, error handling, and human-in-the-loop checkpoints.
Definition
Orchestration in agent engineering refers to the system that coordinates the sequencing, routing, and state management of a multi-step agent workflow. The orchestrator decides:
- Which agent or tool to invoke next.
- What state to pass between steps.
- How to handle errors and retries.
- When to escalate to a human.
- When the overall task is complete.
Orchestration Patterns
Sequential
Steps execute in a fixed order. Each step's output becomes the next step's input. Simple to reason about and debug; limited parallelism.
Parallel
Multiple steps execute concurrently. Requires merging results and handling partial failures. Dramatically faster for independent sub-tasks.
Hierarchical
A supervisor agent decomposes a goal into sub-tasks and delegates to specialized worker agents. The supervisor aggregates worker results and manages the overall plan.
Dynamic (LLM-driven)
The orchestration path is determined by an LLM at runtime based on intermediate results. Maximally flexible; harder to reason about, test, and audit.
Orchestration vs. Agent
An individual agent operates the observe-reason-act loop. An orchestrator manages multiple such loops — or multiple agents — to accomplish a goal too large or complex for a single agent. In practice, the boundary is blurry: a supervisor agent orchestrates workers, and is itself orchestrated by a higher-level system.
Framework Support
Most agent frameworks provide orchestration primitives. LangGraph models orchestration as an explicit graph; AutoGen uses conversational message passing; Pydantic AI expresses it as typed Python control flow.