How to Build a Multi-Agent System for Your Business
A multi-agent system uses multiple specialized AI agents that work together on complex workflows. This guide walks you through designing agent roles, choosing frameworks, setting up communication, and deploying your first multi-agent system.
What Is a Multi-Agent System?
A multi-agent system (MAS) is an architecture where multiple AI agents collaborate to complete tasks that would be difficult or impossible for a single agent. Each agent specializes in a specific domain — research, writing, analysis, data processing — and communicates with other agents to produce a cohesive result.
The concept mirrors how human teams work. A marketing team has a strategist, a writer, a designer, and an analyst. Similarly, a multi-agent system has specialized agents for each function, coordinated by an orchestration layer.
Step 1: Define Your Workflow
Before creating agents, map out the workflow you want to automate. Identify the distinct stages, inputs, outputs, and decision points. Here is an example for a content marketing workflow:
Content Marketing Workflow:
1. Topic Research → Input: niche/keyword
→ Output: topic list with search volume
2. Content Brief → Input: chosen topic
→ Output: outline, target keywords, angle
3. Draft Writing → Input: brief
→ Output: 1,500-word blog post
4. SEO Optimization → Input: draft
→ Output: optimized post + meta tags
5. Publishing → Input: final post
→ Output: live URL on WordPressEach stage becomes a candidate for an agent. Some stages can be combined (research + brief) and some can run in parallel (SEO check while writing).
Step 2: Design Agent Roles
Based on your workflow, define specialized agents. Each agent should have a clear role, specific skills, and well-defined boundaries. A good rule: if a task requires different expertise, it deserves a different agent.
| Agent | Role | Model | Skills |
|---|---|---|---|
| ResearchBot | Topic research, data collection | Claude | Browser, Search API |
| ContentWriter | Blog posts, social copy | Claude | WordPress, Markdown |
| SEOAgent | Keyword optimization, meta tags | GPT-4o | Analytics, Search Console |
| Publisher | Format and publish to CMS | GPT-4o | WordPress API |
Step 3: Choose a Framework
Select a framework or platform based on your technical level and requirements:
| Framework | Technical Level | Best For | Language |
|---|---|---|---|
| CrewClaw | No code | Orchestrating any agent | Markdown (SOUL.md) |
| OpenClaw | Beginner | Single agent creation | Markdown / CLI |
| CrewAI | Intermediate | Role-based agent teams | Python |
| LangGraph | Advanced | Complex graph workflows | Python |
| AutoGen | Advanced | Conversational agents | Python |
Step 4: Create Your Agents
For each agent role, create a SOUL.md file that defines its identity, capabilities, and constraints. Here is an example for a research agent:
# ResearchBot
## Role
You are a research specialist. Your job is to
gather data, analyze competitors, and provide
structured research briefs for the content team.
## Rules
- Always cite sources with URLs
- Present data in tables when possible
- Flag low-confidence findings
- Never fabricate statistics
- Hand off to @ContentWriter when research is complete
## Output Format
- Title, search volume, difficulty
- Top 3 competing articles (URL + word count)
- Suggested angle and unique insights
- Recommended keywords (primary + secondary)
## Tools
- Browser for web research
- Search API for keyword dataNotice the handoff instruction: "Hand off to @ContentWriter when research is complete." This is how agents know who to pass work to in the pipeline. Read the full guide on creating agents with SOUL.md.
Step 5: Set Up Communication
Your agents need a way to share data and coordinate. Set up these communication channels:
Knowledge Base
A shared folder where agents read and write files. Research outputs, drafts, and final assets all live here. Every agent can access it.
Agent Chat
A messaging channel where agents @mention each other. "@ContentWriter research is done, brief is in research-brief.md." This creates an audit trail of agent communication.
Task Board
A kanban-style board where tasks move through stages: Inbox → Assigned → In Progress → Review → Done. Agents pick up tasks assigned to them and update status.
Step 6: Test and Iterate
Deploy your multi-agent system with a real task. Monitor the agent chat to see how agents communicate, check the quality of handoffs, and identify bottlenecks. Common issues to watch for:
- → Agent produces output that the next agent can't parse — fix the output format in SOUL.md
- → Agent goes off-track — add clearer constraints and rules
- → Handoff doesn't trigger — verify the @mention or task routing is configured
- → Quality is inconsistent — add a review agent as a quality gate
Frequently Asked Questions
What is a multi-agent system?
A multi-agent system (MAS) is a setup where multiple AI agents work together to accomplish tasks that are too complex or broad for a single agent. Each agent has a specialized role (like research, writing, or data analysis), and they communicate through shared memory, task handoffs, or direct messaging. The system produces better results because each agent focuses on what it does best.
How many agents do I need to start?
Start with 2-3 agents. A common starter crew is: (1) a research/data agent that gathers information, (2) a content/execution agent that produces the output, and (3) optionally a quality/review agent that checks the work. You can always add more agents as your workflows grow. Starting small helps you understand agent coordination before scaling up.
Which framework should I use for multi-agent systems?
It depends on your technical level. For non-technical users: OpenClaw with SOUL.md files (no code required). For Python developers: CrewAI (simple, role-based) or LangGraph (flexible, graph-based). For enterprise teams: AutoGen (Microsoft) or custom solutions. You can also mix frameworks — platforms like CrewClaw let you orchestrate agents from different frameworks together.
How do agents in a multi-agent system share information?
Agents share information through four main channels: (1) A shared knowledge base — files that all agents can read and write (like brand guides, research data, or drafts). (2) Direct messaging — agents @mention each other to request actions. (3) Task outputs — one agent's result automatically becomes the next agent's input. (4) A shared task board — agents see what tasks are available and claim them.
What are common mistakes when building multi-agent systems?
The most common mistakes are: (1) Making agents too generic — specialist agents outperform generalists. (2) Not defining clear handoff points — agents need to know when to pass work to the next agent. (3) Overcomplicating the architecture — start with a simple sequential pipeline before adding parallel execution. (4) Skipping agent instructions — a SOUL.md with clear rules prevents agents from going off-track. (5) Not testing with real tasks — test with actual workflows, not toy examples.
Can a multi-agent system work with different LLMs?
Yes. In a multi-agent system, each agent can use a different language model. For example, your writing agent might use Claude (best for long-form content), your coding agent might use GPT-4o (fast for code generation), and your data agent might use Gemini (good with structured data). This lets you optimize cost and quality per agent role.
Start building your multi-agent system
CrewClaw makes it easy to connect agents from any framework and orchestrate them as a team.