AI Agent Orchestration: How to Coordinate Multiple Agents
Agent orchestration is the process of coordinating multiple AI agents to work together on complex tasks. This guide covers orchestration patterns, communication models, and how to build your first multi-agent workflow.
What Is Agent Orchestration?
Agent orchestration is the coordination layer that manages how multiple AI agents work together. It handles task assignment, inter-agent communication, dependency management, and result aggregation. Think of it as the operating system for your AI team — individual agents are the workers, and orchestration is the management layer that makes them productive together.
Without orchestration, you are the bottleneck. You copy the output of your research agent, paste it into your writing agent, then manually send the draft to your SEO agent. Orchestration eliminates this manual glue work by letting agents hand off tasks directly.
Why Single Agents Are Not Enough
A single AI agent can be powerful, but it hits limits quickly when tasks span multiple domains. Consider a content marketing workflow:
Single Agent Approach
- One agent tries to research, write, and optimize
- Context window fills up quickly
- Jack of all trades, master of none
- Can't parallelize work
- Single point of failure
Orchestrated Team
- Specialist agents for each domain
- Each agent maintains focused context
- Deep expertise in their role
- Parallel execution where possible
- Failure is isolated to one agent
Orchestration Patterns
There are three fundamental patterns for orchestrating AI agents. Most real-world systems combine two or more of these patterns:
1. Sequential Pipeline
Agents work in a chain. Each agent receives the previous agent's output as input. Best for workflows with clear, ordered stages.
Research Agent → Writing Agent → SEO Agent → Publisher Agent
↓ ↓ ↓ ↓
raw data blog draft optimized post published URL2. Parallel Fan-Out
Multiple agents work simultaneously on independent subtasks. Results are merged by a coordinator. Best for tasks that can be broken into independent pieces.
┌→ Market Research Agent
Goal → Planner → ├→ Competitor Analysis Agent → Merger → Report
└→ Data Collection Agent3. Supervisor Pattern
A coordinator agent manages the workflow, dynamically assigning tasks to specialist agents based on needs. Most flexible but requires a capable supervisor agent.
┌→ Writer Agent
Supervisor → ├→ Research Agent (dynamic routing)
↑↓ ├→ SEO Agent
└→ Data AgentHow Agents Communicate
Effective orchestration depends on how agents share information. The four primary communication mechanisms are:
| Method | How It Works | Best For |
|---|---|---|
| Shared Knowledge Base | Agents read/write to common files | Persistent data sharing |
| @Mentions | Agents tag each other in messages | Direct requests |
| Task Handoffs | Output becomes next agent's input | Sequential pipelines |
| Event Triggers | Actions automatically start other agents | Automated workflows |
Getting Started with Orchestration
To start orchestrating AI agents, you need three things: agents to orchestrate, a communication layer, and a way to define workflows. Here is the simplest path:
Create 2-3 specialized agents with SOUL.md files (e.g., researcher, writer, editor)
Connect them to an orchestration platform like CrewClaw
Define your workflow — which agent handles which part of the task
Set up communication — shared knowledge base and @mention routing
Test with a real task and iterate on agent instructions
Learn how to create individual agents in our agent creation guide, then bring them together as a crew.
Frequently Asked Questions
What is AI agent orchestration?
AI agent orchestration is the process of coordinating multiple AI agents to work together on complex tasks. An orchestration layer manages task assignment, communication between agents, handoffs, and result aggregation — similar to how a project manager coordinates a human team. Instead of running agents in isolation, orchestration creates a unified workflow where agents collaborate.
Why do I need agent orchestration instead of a single agent?
A single agent can handle simple, focused tasks well. But real-world workflows involve multiple domains — research, writing, analysis, publishing — that benefit from specialized agents. Orchestration enables division of labor: a research agent gathers data, a writing agent creates content, and an SEO agent optimizes it. Each agent focuses on what it does best, producing better results than one generalist agent trying to do everything.
What are common agent orchestration patterns?
The three most common patterns are: (1) Sequential pipeline — agents work in a chain, each passing output to the next (research → write → edit → publish). (2) Parallel fan-out — multiple agents work simultaneously on independent subtasks, then results are merged. (3) Supervisor pattern — a coordinator agent (like a PM) assigns tasks to specialist agents and manages the workflow. Most real-world systems combine these patterns.
What tools are available for agent orchestration?
Popular agent orchestration tools include CrewClaw (platform for orchestrating agents from any framework), CrewAI (Python framework for multi-agent systems), LangGraph (graph-based orchestration from LangChain), AutoGen (Microsoft's multi-agent framework), and OpenClaw (open-source agent runtime). The choice depends on your technical level and needs — CrewClaw is ideal for non-technical users, while CrewAI and LangGraph suit developers.
How do agents communicate with each other during orchestration?
Agents communicate through several mechanisms: (1) Shared memory/knowledge base — agents read and write to common files or databases. (2) Direct messaging — agents @mention each other to request actions or share results. (3) Task handoffs — when one agent completes work, it passes the output to the next agent in the pipeline. (4) Event-driven triggers — an agent's action automatically triggers another agent to start work.
Can I orchestrate agents from different frameworks together?
Yes. Platforms like CrewClaw are framework-agnostic — you can connect an OpenClaw agent, a LangChain agent, a CrewAI agent, and a custom API endpoint in the same crew. The orchestration layer handles communication between them regardless of how they were built. This is one of the key advantages of using a dedicated orchestration platform.
Ready to orchestrate your agents?
CrewClaw connects agents from any framework and handles task routing, communication, and handoffs.