AI Blueprint: OpenClaw Multi-Agent Orchestration - The Supervisor Pattern

Objective: Deploy a coordinated team of OpenClaw agents using the supervisor pattern for enterprise workflow automation.

Prerequisites: OpenClaw installation, Docker, basic understanding of YAML configuration.

Complexity: Intermediate to Advanced
Implementation Time: 2-4 weeks
Team Size: 2-3 technical staff

The Supervisor Pattern Explained

Instead of one monolithic agent handling all tasks, the supervisor pattern uses:

• 1 Supervisor Agent: Coordinates work, makes decisions about task assignment
• 3-7 Specialist Agents: Each optimised for specific functions (research, writing, analysis, etc.)
• 1 Quality Assurance Agent: Reviews output from specialist agents before delivery

This mirrors how human teams work: a project manager coordinating specialists who focus on their expertise areas.

Architecture Overview

┌─────────────────┐    ┌─────────────────┐
│   Client/User   │────│  Supervisor     │
└─────────────────┘    │  Agent          │
                       └─────┬───────────┘
                             │
                   ┌─────────┼─────────┐
                   │         │         │
            ┌──────▼──┐ ┌────▼───┐ ┌──▼──────┐
            │Research │ │Content │ │Analysis │
            │Agent    │ │Agent   │ │Agent    │
            └─────────┘ └────────┘ └─────────┘
                   │         │         │
                   └─────────┼─────────┘
                             │
                      ┌──────▼──────┐
                      │QA Agent     │
                      └─────────────┘

Step 1: Agent Configuration Files

Supervisor Agent Configuration

# supervisor-agent.yml
agent:
  name: "supervisor"
  role: "coordination"
  model: "claude-3-5-sonnet"
  
system_prompt: |
  You are the Supervisor Agent for a multi-agent business automation system.
  
  Your responsibilities:
  - Receive user requests and break them into component tasks
  - Assign tasks to appropriate specialist agents
  - Monitor task progress and quality
  - Coordinate handoffs between agents
  - Deliver final results to users
  
  Available specialist agents:
  - research-agent: Market research, data gathering, competitive analysis
  - content-agent: Writing, editing, document creation
  - analysis-agent: Data analysis, financial modeling, strategic assessment
  - qa-agent: Quality review, fact-checking, consistency validation
  
  NEVER execute specialist tasks yourself. Always delegate to appropriate agents.
  Always request QA review before delivering final output.

tools:
  - name: "delegate_task"
    description: "Assign a task to a specialist agent"
    parameters:
      agent_id: 
        type: "string"
        required: true
      task_description:
        type: "string" 
        required: true
      priority:
        type: "string"
        enum: ["low", "medium", "high", "urgent"]
      deadline:
        type: "string"
        
  - name: "check_agent_status"
    description: "Check the status of work assigned to other agents"
    
  - name: "request_qa_review"
    description: "Send completed work to QA agent for final review"

security:
  sandbox: true
  network_access: limited
  file_permissions: read-write
  max_concurrent_tasks: 10

Research Agent Configuration

# research-agent.yml  
agent:
  name: "research-agent"
  role: "research"
  model: "claude-3-5-sonnet"
  
system_prompt: |
  You are the Research Agent specialising in data gathering and market analysis.
  
  Your capabilities:
  - Market research and competitive intelligence
  - Data collection from approved sources
  - Industry trend analysis  
  - Financial and business data compilation
  - Fact verification and source validation
  
  Always provide sources for your research findings.
  Flag any data that cannot be independently verified.
  Report back to supervisor with structured findings.

tools:
  - name: "web_search"
    rate_limit: "100_per_hour"
    
  - name: "data_extraction"
    approved_domains:
      - "companies-house.gov.uk"
      - "gov.uk"
      - "ons.gov.uk"
      - "ft.com"
      - "economist.com"
      
  - name: "report_findings"
    description: "Send research results back to supervisor"

security:
  sandbox: true  
  network_access: whitelist
  approved_domains: 
    - research-databases
    - public-data-sources
  max_requests_per_hour: 500

Content Agent Configuration

# content-agent.yml
agent:
  name: "content-agent"  
  role: "content-creation"
  model: "claude-3-5-sonnet"
  
system_prompt: |
  You are the Content Agent specialising in writing and document creation.
  
  Your capabilities:
  - Business document writing (reports, proposals, emails)
  - Content editing and improvement
  - Format conversion and styling
  - Brand voice consistency
  - Multi-format output (PDF, Word, HTML, Markdown)
  
  Always maintain professional UK business writing standards.
  Follow brand guidelines provided in each request.
  Request additional information if brief is unclear.

tools:
  - name: "document_creation"
    formats: ["pdf", "docx", "html", "markdown"]
    
  - name: "style_guide_check" 
    description: "Validate content against brand guidelines"
    
  - name: "collaboration_review"
    description: "Request input from other agents for content accuracy"

security:
  sandbox: true
  file_permissions: read-write
  template_directory: "/approved-templates"
  max_document_size: "50MB"

Analysis Agent Configuration

# analysis-agent.yml
agent:
  name: "analysis-agent"
  role: "data-analysis"  
  model: "claude-3-5-sonnet"
  
system_prompt: |
  You are the Analysis Agent specialising in data analysis and strategic assessment.
  
  Your capabilities:
  - Financial modeling and analysis
  - Business performance metrics
  - Strategic recommendations  
  - Risk assessment
  - Trend analysis and forecasting
  
  Always show your working and assumptions.
  Provide confidence levels for predictions.
  Highlight limitations and risks in your analysis.

tools:
  - name: "data_processing"
    max_dataset_size: "100MB"
    supported_formats: ["csv", "xlsx", "json"]
    
  - name: "financial_modeling"
    description: "Create financial models and projections"
    
  - name: "statistical_analysis"
    libraries: ["pandas", "numpy", "scipy"]
    
security:
  sandbox: true
  compute_limits:
    memory: "8GB"
    cpu_time: "30min"
  data_access: encrypted

QA Agent Configuration

# qa-agent.yml
agent:
  name: "qa-agent"
  role: "quality-assurance"
  model: "claude-3-5-sonnet"
  
system_prompt: |
  You are the Quality Assurance Agent responsible for final output review.
  
  Your responsibilities:
  - Fact-checking and accuracy verification
  - Consistency checking across deliverables  
  - Brand compliance validation
  - Completeness assessment
  - Final approval or rejection with feedback
  
  NEVER approve work that has factual errors.
  Always provide specific feedback for rejected work.
  Maintain quality standards even under time pressure.

tools:
  - name: "fact_verification"
    description: "Cross-reference facts against reliable sources"
    
  - name: "consistency_check"
    description: "Validate consistency within and across documents"
    
  - name: "quality_scoring"
    description: "Assign quality scores with detailed feedback"
    
  - name: "final_approval"
    description: "Approve or reject completed work"

security:
  sandbox: true
  access_level: "review_only"
  approval_required: true

Step 2: Docker Deployment Configuration

# docker-compose.yml
version: '3.8'

services:
  supervisor-agent:
    image: openclaw/agent:latest
    container_name: supervisor-agent
    volumes:
      - ./configs/supervisor-agent.yml:/config/agent.yml
      - ./shared:/shared
    environment:
      - AGENT_CONFIG=/config/agent.yml
      - SHARED_WORKSPACE=/shared
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    networks:
      - agent-network
    restart: unless-stopped
    
  research-agent:
    image: openclaw/agent:latest  
    container_name: research-agent
    volumes:
      - ./configs/research-agent.yml:/config/agent.yml
      - ./shared:/shared:ro
    environment:
      - AGENT_CONFIG=/config/agent.yml
      - SHARED_WORKSPACE=/shared
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    networks:
      - agent-network
      - research-network
    restart: unless-stopped
    
  content-agent:
    image: openclaw/agent:latest
    container_name: content-agent
    volumes:
      - ./configs/content-agent.yml:/config/agent.yml
      - ./shared:/shared
      - ./templates:/templates:ro
    environment:
      - AGENT_CONFIG=/config/agent.yml
      - SHARED_WORKSPACE=/shared
      - TEMPLATE_DIR=/templates
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    networks:
      - agent-network
    restart: unless-stopped
    
  analysis-agent:
    image: openclaw/agent:latest
    container_name: analysis-agent  
    volumes:
      - ./configs/analysis-agent.yml:/config/agent.yml
      - ./shared:/shared
    environment:
      - AGENT_CONFIG=/config/agent.yml
      - SHARED_WORKSPACE=/shared
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    networks:
      - agent-network
    restart: unless-stopped
    deploy:
      resources:
        limits:
          memory: 8G
          cpus: '4'
          
  qa-agent:
    image: openclaw/agent:latest
    container_name: qa-agent
    volumes:
      - ./configs/qa-agent.yml:/config/agent.yml  
      - ./shared:/shared:ro
    environment:
      - AGENT_CONFIG=/config/agent.yml
      - SHARED_WORKSPACE=/shared
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    networks:
      - agent-network
    restart: unless-stopped

networks:
  agent-network:
    driver: bridge
    internal: false
    ipam:
      config:
        - subnet: 172.20.0.0/16
  research-network:
    driver: bridge
    internal: false
    
volumes:
  shared:
    driver: local

Step 3: Coordination Workflow Implementation

# coordination.py - Supervisor agent coordination logic
class SupervisorOrchestrator:
    def __init__(self):
        self.agents = {
            'research': ResearchAgentClient(),
            'content': ContentAgentClient(), 
            'analysis': AnalysisAgentClient(),
            'qa': QAAgentClient()
        }
        self.task_queue = TaskQueue()
        self.workflow_state = WorkflowState()
        
    async def process_request(self, user_request):
        # Break down user request into component tasks
        task_breakdown = await self.analyze_request(user_request)
        
        # Create execution plan
        execution_plan = self.create_execution_plan(task_breakdown)
        
        # Execute tasks in coordinated sequence  
        results = await self.execute_plan(execution_plan)
        
        # QA review
        final_output = await self.qa_review(results)
        
        return final_output
        
    async def execute_plan(self, plan):
        results = {}
        
        for phase in plan.phases:
            phase_results = await asyncio.gather(*[
                self.delegate_task(task.agent, task.description, task.inputs)
                for task in phase.tasks
            ])
            
            results[phase.name] = phase_results
            
            # Update workflow state for next phase
            self.workflow_state.update(phase.name, phase_results)
            
        return results
        
    async def delegate_task(self, agent_name, task_description, inputs):
        agent = self.agents[agent_name]
        
        task_result = await agent.execute_task({
            'description': task_description,
            'inputs': inputs,
            'context': self.workflow_state.get_context(),
            'deadline': self.calculate_deadline(task_description)
        })
        
        return task_result

Step 4: Security Configuration

# security-policies.yml
network_policies:
  supervisor_agent:
    outbound:
      - target: "research-agent:8080"
      - target: "content-agent:8080" 
      - target: "analysis-agent:8080"
      - target: "qa-agent:8080"
    inbound:
      - source: "client-network"
      
  research_agent:
    outbound:
      - target: "approved-research-apis"
      - target: "public-data-sources"
    inbound:
      - source: "supervisor-agent"
      
  content_agent:
    outbound: []  # No external network access
    inbound:
      - source: "supervisor-agent"
      
  analysis_agent:
    outbound: []  # No external network access  
    inbound:
      - source: "supervisor-agent"
      
  qa_agent:
    outbound:
      - target: "fact-checking-apis"
    inbound:
      - source: "supervisor-agent"

resource_limits:
  supervisor_agent:
    memory: "2GB"
    cpu: "1.0"
    storage: "10GB"
    
  research_agent:
    memory: "4GB" 
    cpu: "2.0"
    storage: "50GB"
    
  content_agent:
    memory: "2GB"
    cpu: "1.0" 
    storage: "20GB"
    
  analysis_agent:
    memory: "8GB"
    cpu: "4.0"
    storage: "100GB"
    
  qa_agent:
    memory: "1GB"
    cpu: "0.5"
    storage: "5GB"

Step 5: Monitoring and Observability

# monitoring.yml
prometheus:
  scrape_configs:
    - job_name: 'openclaw-agents'
      static_configs:
        - targets: 
          - 'supervisor-agent:9090'
          - 'research-agent:9090'
          - 'content-agent:9090'
          - 'analysis-agent:9090'
          - 'qa-agent:9090'
          
  alerts:
    - name: "agent_down"
      condition: "up == 0"
      for: "30s"
      
    - name: "task_queue_backup" 
      condition: "task_queue_depth > 50"
      for: "5m"
      
    - name: "qa_rejection_rate_high"
      condition: "qa_rejection_rate > 0.3"
      for: "10m"

grafana:
  dashboards:
    - "multi-agent-overview.json"
    - "task-coordination-metrics.json" 
    - "quality-metrics.json"
    - "resource-utilisation.json"

logging:
  structured_format: true
  fields:
    - timestamp
    - agent_id
    - task_id
    - workflow_id
    - user_id
    - action
    - duration_ms
    - status
    - error_details

Step 6: Testing and Validation

#!/bin/bash
# test-orchestration.sh

echo "Testing multi-agent orchestration..."

# Test 1: Simple delegation
echo "Test 1: Task delegation"
curl -X POST http://supervisor-agent:8080/task \
  -H "Content-Type: application/json" \
  -d '{
    "request": "Research the UK fintech market and create a 5-page analysis report",
    "deadline": "2 hours",
    "quality_level": "high"
  }'

# Test 2: Cross-agent coordination  
echo "Test 2: Complex workflow"
curl -X POST http://supervisor-agent:8080/workflow \
  -H "Content-Type: application/json" \
  -d '{
    "request": "Analyse our Q4 performance, research competitor strategies, and create board presentation",
    "phases": ["research", "analysis", "content", "qa"],
    "deadline": "4 hours"
  }'

# Test 3: QA rejection handling
echo "Test 3: QA feedback loop" 
curl -X POST http://supervisor-agent:8080/task \
  -H "Content-Type: application/json" \
  -d '{
    "request": "Create marketing copy with intentional errors for QA testing",
    "include_errors": true,
    "test_mode": true
  }'

echo "Tests complete. Check logs for results."

Implementation Checklist

• Week 1: Configure individual agent containers
• Week 1: Set up shared storage and networking
• Week 2: Implement supervisor coordination logic
• Week 2: Configure security policies and resource limits
• Week 3: Deploy monitoring and logging
• Week 3: Run integration tests
• Week 4: Performance tuning and optimization
• Week 4: Create runbooks and documentation

Success Metrics

• Task Completion Rate: >95% of delegated tasks complete successfully
• QA Pass Rate: >80% of work passes QA on first review
• Response Time: <5 minutes for simple tasks, <30 minutes for complex workflows
• Resource Utilisation: <70% average CPU/memory across all agents
• Error Rate: <2% system errors, <5% task failures

Troubleshooting Common Issues

Agent Communication Failures:

• Check network policies and firewall rules
• Verify shared storage permissions
• Review container logs for connection errors

QA Bottlenecks:

• Scale QA agent resources
• Implement parallel QA review for independent sections
• Add pre-QA validation in specialist agents

Task Assignment Loops:

• Review supervisor prompts for clear delegation logic
• Add circuit breakers for retry attempts
• Implement task timeout mechanisms

Security Violations:

• Audit agent tool usage logs
• Review and tighten network access policies
• Implement additional approval workflows for high-risk operations

This blueprint provides the foundation for enterprise-grade multi-agent orchestration. Scale agent counts and specialisation based on your specific workflow requirements.

Need help implementing multi-agent OpenClaw systems? We've deployed coordinated agent teams for UK enterprises across finance, consulting, and manufacturing. Get implementation support.