Why Multi-Agent Workflows?
A single AI agent is powerful. But complex tasks — the kind that create real business value — often require multiple specialized agents working together. Think of it like a company: you don't have one person doing sales, engineering, marketing, and accounting. You have specialists who collaborate.
Multi-agent workflows let you:
- Specialize: Each agent focuses on what it does best
- Parallelize: Run independent tasks simultaneously
- Quality-check: Have one agent review another's work
- Scale: Add capacity to bottleneck stages without redesigning everything
Workflow Architecture Patterns
Sequential Pipeline
The simplest pattern: Agent A → Agent B → Agent C. Each agent processes the output of the previous one.
Example: Research Agent → Analysis Agent → Writing Agent for content production.
Parallel Fan-Out / Fan-In
Split a task across multiple agents, then merge results.
Example: A lead enrichment workflow that sends a company name to 3 agents simultaneously — one researches the company, one finds contacts, one analyzes competitors — then a fourth agent synthesizes everything into a report.
Supervisor Pattern
One "supervisor" agent coordinates other agents, assigning tasks and reviewing outputs.
Example: A QA workflow where a supervisor agent assigns testing tasks to worker agents, reviews results, and decides if more testing is needed.
Router Pattern
An agent examines incoming tasks and routes them to the appropriate specialist agent.
Example: A customer support system where a router agent categorizes incoming tickets and dispatches them to billing, technical, or general support agents.
Building Your First Workflow: Content Production Pipeline
Let's build a practical example: a 3-agent content production pipeline.
Agent 1: Researcher
Responsible for gathering information on a topic. Uses web search and document analysis skills.
openclaw init --name content-researcher
// Skills: web-search, doc-read, summarize
// Model: Claude Sonnet (good balance of speed and quality)
Agent 2: Writer
Takes research and produces draft content. Specialized in long-form writing.
openclaw init --name content-writer
// Skills: long-form-write, seo-optimize
// Model: Claude Opus (best quality for writing)
Agent 3: Editor
Reviews and polishes the draft. Checks for accuracy, tone, and formatting.
openclaw init --name content-editor
// Skills: grammar-check, fact-verify, format
// Model: GPT-4o (great at editing and correction)
Connecting the Workflow
Use OpenClaw's workflow definition to connect your agents:
openclaw workflow create content-pipeline \
--step researcher "Research the topic: {input}" \
--step writer "Write an article based on: {researcher.output}" \
--step editor "Edit and polish: {writer.output}"
Managing Workflows in ClawHQ
Once your workflow is running, ClawHQ provides:
- Visual flow: See the data moving between agents in real time
- Per-agent metrics: Identify which agent is the bottleneck
- Error handling: Configure retry policies per step
- Cost breakdown: See cost per workflow run, broken down by agent
Best Practices for Multi-Agent Workflows
- Start simple: Begin with 2-3 agents. Add complexity only when needed.
- Define clear interfaces: Each agent should have well-defined inputs and outputs.
- Use the right model for each job: Don't use an expensive model for simple tasks.
- Build in error handling: What happens if one agent fails? Define retry and fallback strategies.
- Monitor everything: Use ClawHQ's monitoring to track workflow performance.
- Test with small batches: Run a few tasks through the workflow before scaling up.
Scaling Your Workflows
Once your workflow is running well, you can scale it by:
- Horizontal scaling: Run multiple instances of bottleneck agents
- Queue management: Use ClawHQ's task queue to buffer work between agents
- Parallel processing: Convert sequential steps to parallel where possible
For a deeper dive on scaling, check out how to scale from 1 to 50 agents.
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