Multi-agent orchestration
Overview
Multi-agent orchestration refers to the coordination of two or more autonomous or semi-autonomous AI agents — each with its own context, tools, and objectives — to accomplish a task that would be impractical or inefficient for a single agent. The orchestration layer manages how agents are invoked, how their outputs are routed, and how conflicts or failures are resolved.
Multi-agent systems have been used in software for decades, but LLM-based multi-agent orchestration emerged as a practical pattern in 2023–2024, driven by the ability to direct agents with natural language and to compose tool-using (tool use) agents into pipelines without explicit hard-coded logic.
The field lacks settled vocabulary: agent coordination, agentic orchestration, and multi-agent systems are used interchangeably. This page treats orchestration as the coordination activity and multi-agent system as the resulting architecture.
Orchestration patterns
| Pattern | Description | When used |
|---|---|---|
| Sequential pipeline | Agent A output → Agent B input → … | Tasks with ordered, dependent stages |
| Parallel fan-out | Multiple agents tackle subtasks concurrently; results merged | Independent subtasks; research gathering |
| Hierarchical (orchestrator–worker) | Orchestrator agent decomposes task, delegates to workers, synthesizes | Complex planning with heterogeneous subtasks |
| Inter-agent review | Agent A produces draft; Agent B critiques; repeat | Quality-sensitive outputs |
Orchestrator vs worker agents
In hierarchical architectures:
- The orchestrator receives the high-level goal, decomposes it into subtasks, and routes them to workers.
- Worker agents have narrower scope — a specialist coder, a retriever, a verifier — and report results back.
The orchestrator may itself be an LLM that reasons about delegation using Chain-of-thought or ReAct patterns.
Distinction from a single agentic loop
| Dimension | Single agent (Agentic workflow) | Multi-agent orchestration |
|---|---|---|
| Agent count | One | Two or more |
| Context isolation | Single context window | Each agent has its own context |
| Parallelism | Sequential tool calls | Possible concurrent agent execution |
| Failure scope | One failure surface | Failures can be isolated or cascade |
A single ReAct-style agent with many tool calls is not multi-agent orchestration. Multi-agent requires distinct agent instances with separate contexts.
Frameworks
Common frameworks implementing multi-agent orchestration as of 2024–2025 include LangGraph, AutoGen (Microsoft), CrewAI, and the Anthropic Agent SDK. Framework-level implementations vary in how they handle message passing, state management, and failure.
