# Multi-Agent Coordination Operating Procedures

## Overview

This document provides standard operating procedures (SOPs) for operating the multi-agent coordination system, including day-to-day operations, troubleshooting, and emergency procedures.

## 1. Standard Operating Procedures

### 1.1 Deployment Workflow Operations

#### Standardized Deployment Procedure
The framework now includes a standardized 5-step deployment workflow that must be followed for all managed projects:

1. **Ops Agent**: Local server deployment + health check
2. **Ops Agent**: Automatic browser launch to deployment URL
3. **QA Agent**: Screenshot capture + visual verification
4. **QA Agent**: Deployment success documentation
5. **Framework**: Handoff to development agents

**Reference**: See `/framework/templates/DEPLOYMENT_WORKFLOW.md` for complete workflow template.

#### Browser Auto-Launch Standards
- **Command**: `open -a "Microsoft Edge" [URL]` (macOS standard)
- **Timing**: Only after successful server health verification
- **Coordination**: Must notify QA Agent immediately after launch
- **Documentation**: Record browser launch timestamp and URL

#### QA Screenshot Verification Requirements
- **Timing**: Immediately after receiving browser launch notification
- **Content**: Full-page screenshot of deployed application
- **Verification**: Visual check for errors, UI issues, functionality
- **Documentation**: Save screenshot evidence with timestamp and verification report

### 1.2 Daily Operations

#### Morning Startup Checklist
```bash
# Check system health
./scripts/check_coordination_health.sh

# Review overnight activities
./scripts/review_overnight_logs.sh

# Check resource availability
./scripts/check_resource_status.sh

# Validate agent connectivity
./scripts/validate_agent_connectivity.sh
```

#### Agent Assignment Procedure
1. **Analyze Task Requirements**
   ```python
   # Use the task analyzer to determine requirements
   task_analyzer = TaskAnalyzer()
   requirements = task_analyzer.analyze(task_description)
   ```

2. **Check Agent Availability**
   ```python
   # Check which agents are available
   agent_manager = AgentManager()
   available_agents = agent_manager.get_available_agents()
   ```

3. **Assign Optimal Agents**
   ```python
   # Use the coordinator to assign agents
   coordinator = WorkflowCoordinator()
   assignment = coordinator.assign_agents(requirements, available_agents)
   ```

4. **Validate Assignment**
   ```python
   # Validate the assignment meets policies
   policy_enforcer = CoordinationPolicyEnforcer()
   violations = policy_enforcer.validate_agent_assignment(assignment)
   if violations:
       handle_policy_violations(violations)
   ```

### 1.2 Conflict Management Procedures

#### Conflict Detection Protocol
1. **Monitor Conflict Alerts**
   - Check coordination dashboard every 15 minutes
   - Review conflict detection logs
   - Respond to automatic alerts

2. **Conflict Assessment**
   ```python
   # Assess conflict severity and impact
   conflict_assessor = ConflictAssessor()
   assessment = conflict_assessor.assess(conflict)
   
   if assessment.severity == "critical":
       escalate_immediately(conflict)
   elif assessment.severity == "high":
       attempt_automatic_resolution(conflict)
   else:
       queue_for_routine_resolution(conflict)
   ```

3. **Resolution Execution**
   ```python
   # Execute appropriate resolution strategy
   resolver = ConflictResolver()
   resolution = await resolver.resolve_conflict(conflict)
   
   if resolution.successful:
       log_successful_resolution(conflict, resolution)
   else:
       escalate_conflict(conflict, resolution.failure_reason)
   ```

#### Escalation Decision Tree
```yaml
Escalation_Decisions:
  conflict_duration_30min:
    action: "escalate_to_orchestrator"
    reason: "Automatic resolution timeout"
    
  conflict_duration_2hr:
    action: "escalate_to_human"
    reason: "Orchestrator resolution timeout"
    
  critical_security_issue:
    action: "immediate_human_escalation"
    reason: "Security risk requires immediate attention"
    
  resource_exhaustion:
    action: "emergency_resource_reallocation"
    reason: "System stability at risk"
    
  multiple_agent_failure:
    action: "activate_safe_mode"
    reason: "Cascade failure prevention"
```

### 1.3 Resource Management Procedures

#### Resource Allocation Process
1. **Assess Resource Requirements**
   ```python
   # Predict resource needs
   resource_predictor = ResourcePredictor()
   predicted_needs = resource_predictor.predict_resource_needs(task)
   ```

2. **Check Resource Availability**
   ```python
   # Check current resource status
   resource_manager = ResourceManager()
   availability = resource_manager.get_resource_availability()
   ```

3. **Optimize Resource Usage**
   ```python
   # Optimize if resources are constrained
   if not resource_manager.can_allocate(predicted_needs):
       optimization_result = await resource_manager.optimize_resource_usage()
       if not optimization_result.successful:
           queue_task_for_later(task, "insufficient_resources")
   ```

4. **Allocate and Monitor**
   ```python
   # Allocate resources and set up monitoring
   allocation = await resource_manager.allocate_resources(agent_id, predicted_needs)
   monitor_resource_usage(agent_id, allocation)
   ```

#### Resource Optimization Checklist
- [ ] Identify idle agents and release their resources
- [ ] Compress storage usage where possible
- [ ] Optimize memory allocation patterns
- [ ] Balance CPU usage across available cores
- [ ] Clean up temporary files and caches

### 1.4 Performance Monitoring Procedures

#### Daily Performance Review
1. **Check Key Metrics**
   ```python
   # Collect daily performance summary
   performance_monitor = PerformanceMonitor()
   daily_summary = performance_monitor.get_daily_summary()
   
   # Key metrics to review:
   # - Agent utilization rates
   # - Conflict resolution times
   # - Resource efficiency
   # - Task completion rates
   ```

2. **Identify Performance Issues**
   ```python
   # Look for performance anomalies
   anomalies = performance_monitor.detect_anomalies(daily_summary)
   for anomaly in anomalies:
       investigate_anomaly(anomaly)
   ```

3. **Generate Performance Report**
   ```python
   # Generate and distribute daily report
   report_generator = ReportGenerator()
   daily_report = report_generator.generate_daily_report(daily_summary)
   distribute_report(daily_report)
   ```

#### Performance Optimization Actions
- **High Agent Utilization (>95%)**
  - Scale up agent instances if possible
  - Implement task queueing
  - Consider load balancing improvements

- **High Coordination Overhead (>5%)**
  - Review coordination algorithms
  - Optimize communication protocols
  - Consider caching improvements

- **Low Resource Efficiency (<85%)**
  - Analyze resource allocation patterns
  - Implement better resource prediction
  - Optimize resource sharing strategies

## 2. Troubleshooting Procedures

### 2.1 Common Issues and Solutions

#### Agent Connectivity Issues
**Symptoms**: Agents not responding, timeout errors
**Diagnosis**:
```bash
# Check agent connectivity
./scripts/check_agent_connectivity.sh

# Review agent logs
tail -f logs/agents/*.log

# Check network connectivity
ping agent-host
telnet agent-host agent-port
```

**Solutions**:
1. Restart unresponsive agents
2. Check network configuration
3. Verify agent authentication
4. Review resource constraints

#### Memory Integration Problems
**Symptoms**: Memory retrieval timeouts, pattern matching failures
**Diagnosis**:
```python
# Test memory system connectivity
memory_client = MemoryClient()
test_result = memory_client.health_check()

# Check memory system logs
check_memory_system_logs()

# Verify memory schema
validate_memory_schema()
```

**Solutions**:
1. Restart memory service
2. Clear corrupted memory cache
3. Rebuild memory indices
4. Check memory system capacity

#### Resource Exhaustion
**Symptoms**: Task failures, resource allocation errors
**Diagnosis**:
```python
# Check resource usage
resource_manager = ResourceManager()
usage_report = resource_manager.generate_usage_report()

# Identify resource hogs
heavy_users = resource_manager.identify_heavy_users()

# Check for resource leaks
leaks = resource_manager.detect_resource_leaks()
```

**Solutions**:
1. Kill resource-heavy processes
2. Clear temporary files
3. Restart agents with resource leaks
4. Scale up infrastructure if needed

### 2.2 Emergency Procedures

#### System-Wide Agent Failure
**Immediate Actions**:
1. Activate safe mode
2. Stop new task assignments
3. Preserve current state
4. Notify stakeholders

**Recovery Steps**:
```bash
# Enter safe mode
./scripts/enter_safe_mode.sh

# Diagnose failure cause
./scripts/diagnose_system_failure.sh

# Restart agents incrementally
./scripts/restart_agents_incrementally.sh

# Validate system recovery
./scripts/validate_system_recovery.sh

# Exit safe mode
./scripts/exit_safe_mode.sh
```

#### Memory System Corruption
**Immediate Actions**:
1. Stop memory writes
2. Backup current memory state
3. Switch to backup memory system
4. Assess corruption extent

**Recovery Steps**:
```bash
# Stop memory writes
./scripts/stop_memory_writes.sh

# Backup current state
./scripts/backup_memory_state.sh

# Assess corruption
./scripts/assess_memory_corruption.sh

# Restore from backup
./scripts/restore_memory_backup.sh

# Validate memory integrity
./scripts/validate_memory_integrity.sh
```

#### Security Breach Detection
**Immediate Actions**:
1. Isolate affected agents
2. Stop all external communications
3. Preserve evidence
4. Notify security team

**Recovery Steps**:
```bash
# Isolate affected systems
./scripts/isolate_affected_agents.sh

# Stop external communications
./scripts/stop_external_comms.sh

# Collect security evidence
./scripts/collect_security_evidence.sh

# Run security scan
./scripts/run_security_scan.sh

# Implement security patches
./scripts/apply_security_patches.sh
```

### 2.3 Diagnostic Tools

#### System Health Checks
```python
# framework/coordination/diagnostics.py
class SystemDiagnostics:
    def __init__(self):
        self.health_checks = [
            self.check_agent_health,
            self.check_memory_health,
            self.check_resource_health,
            self.check_coordination_health,
            self.check_network_health
        ]
        
    def run_full_diagnostics(self):
        """Run complete system diagnostics"""
        results = {}
        for check in self.health_checks:
            try:
                results[check.__name__] = check()
            except Exception as e:
                results[check.__name__] = {
                    "status": "failed",
                    "error": str(e)
                }
        return results
        
    def check_agent_health(self):
        """Check health of all agents"""
        agent_status = {}
        for agent_id in self.get_all_agents():
            try:
                response = self.ping_agent(agent_id)
                agent_status[agent_id] = {
                    "status": "healthy",
                    "response_time": response.time,
                    "version": response.version
                }
            except Exception as e:
                agent_status[agent_id] = {
                    "status": "unhealthy",
                    "error": str(e)
                }
        return agent_status
```

#### Performance Analysis Tools
```python
class PerformanceAnalyzer:
    def analyze_bottlenecks(self):
        """Analyze system bottlenecks"""
        bottlenecks = []
        
        # Check agent bottlenecks
        agent_metrics = self.get_agent_metrics()
        for agent_id, metrics in agent_metrics.items():
            if metrics["queue_length"] > 10:
                bottlenecks.append({
                    "type": "agent_queue",
                    "agent": agent_id,
                    "queue_length": metrics["queue_length"]
                })
                
        # Check resource bottlenecks
        resource_usage = self.get_resource_usage()
        for resource, usage in resource_usage.items():
            if usage > 0.9:
                bottlenecks.append({
                    "type": "resource_contention",
                    "resource": resource,
                    "usage": usage
                })
                
        return bottlenecks
        
    def generate_performance_report(self):
        """Generate comprehensive performance report"""
        return {
            "bottlenecks": self.analyze_bottlenecks(),
            "agent_performance": self.analyze_agent_performance(),
            "resource_efficiency": self.analyze_resource_efficiency(),
            "coordination_overhead": self.calculate_coordination_overhead(),
            "recommendations": self.generate_recommendations()
        }
```

## 3. Maintenance Procedures

### 3.1 Regular Maintenance Tasks

#### Daily Maintenance
- [ ] Review overnight logs for errors
- [ ] Check resource usage trends
- [ ] Verify agent connectivity
- [ ] Monitor conflict resolution metrics
- [ ] Clean up temporary files

#### Weekly Maintenance
- [ ] Analyze performance trends
- [ ] Review memory system efficiency
- [ ] Update coordination patterns
- [ ] Backup system configuration
- [ ] Test disaster recovery procedures

#### Monthly Maintenance
- [ ] Comprehensive performance review
- [ ] Update documentation
- [ ] Review and update policies
- [ ] Capacity planning assessment
- [ ] Security audit

### 3.2 Maintenance Scripts

#### Daily Cleanup Script
```bash
#!/bin/bash
# scripts/daily_cleanup.sh

echo "Starting daily cleanup..."

# Clean temporary files
find /tmp/coordination -type f -mtime +1 -delete

# Rotate logs
logrotate /etc/logrotate.d/coordination

# Clean old memory snapshots
find /var/lib/coordination/memory/snapshots -mtime +7 -delete

# Optimize database
/usr/local/bin/coordination-db-optimize

echo "Daily cleanup completed"
```

#### Performance Analysis Script
```bash
#!/bin/bash
# scripts/analyze_performance.sh

echo "Analyzing coordination system performance..."

# Generate performance report
python3 -c "
from framework.coordination.diagnostics import PerformanceAnalyzer
analyzer = PerformanceAnalyzer()
report = analyzer.generate_performance_report()
print(report)
"

# Check for bottlenecks
python3 -c "
from framework.coordination.diagnostics import PerformanceAnalyzer
analyzer = PerformanceAnalyzer()
bottlenecks = analyzer.analyze_bottlenecks()
if bottlenecks:
    print('BOTTLENECKS DETECTED:')
    for bottleneck in bottlenecks:
        print(f'  {bottleneck}')
else:
    print('No bottlenecks detected')
"
```

### 3.3 Update Procedures

#### Coordination System Updates
1. **Preparation**
   - Backup current configuration
   - Test update in staging environment
   - Prepare rollback plan
   - Schedule maintenance window

2. **Update Execution**
   ```bash
   # Stop coordination services
   ./scripts/stop_coordination_services.sh
   
   # Backup current state
   ./scripts/backup_coordination_state.sh
   
   # Apply updates
   ./scripts/apply_coordination_updates.sh
   
   # Start services
   ./scripts/start_coordination_services.sh
   
   # Validate update
   ./scripts/validate_update.sh
   ```

3. **Post-Update Validation**
   - Run health checks
   - Verify all agents are responsive
   - Test conflict resolution
   - Monitor performance metrics

## 4. Monitoring and Alerting

### 4.1 Alert Configurations

#### Critical Alerts (Immediate Response)
- System-wide agent failure
- Memory system corruption
- Security breach detection
- Resource exhaustion
- Cascade failure detection

#### High Priority Alerts (1 hour response)
- Multiple agent failures
- High conflict resolution times
- Resource allocation failures
- Performance degradation
- Memory integration issues

#### Medium Priority Alerts (4 hour response)
- Individual agent failures
- Resource optimization opportunities
- Performance anomalies
- Configuration drift
- Capacity warnings

### 4.2 Monitoring Dashboards

#### Operations Dashboard
- Real-time agent status
- Active conflicts and resolutions
- Resource utilization
- System health indicators
- Recent alerts and incidents

#### Performance Dashboard
- Agent performance metrics
- Coordination overhead trends
- Resource efficiency metrics
- Task completion rates
- Bottleneck analysis

#### Capacity Dashboard
- Resource usage trends
- Growth projections
- Capacity recommendations
- Scaling opportunities
- Cost optimization insights

## 5. Documentation and Knowledge Management

### 5.1 Runbook Maintenance
- Keep procedures updated with system changes
- Document lessons learned from incidents
- Maintain troubleshooting knowledge base
- Update contact information regularly

### 5.2 Training and Knowledge Transfer
- Regular training on new procedures
- Cross-training for critical procedures
- Documentation of tribal knowledge
- Knowledge sharing sessions

### 5.3 Incident Documentation
- Document all significant incidents
- Perform post-incident reviews
- Update procedures based on lessons learned
- Share learnings across teams

---

## Quick Reference

### Emergency Contacts
- **System Administrator**: [contact-info]
- **Security Team**: [contact-info]
- **Development Team**: [contact-info]
- **Business Stakeholders**: [contact-info]

### Key Commands
```bash
# System health check
./scripts/health_check.sh

# Emergency safe mode
./scripts/emergency_safe_mode.sh

# Resource status
./scripts/resource_status.sh

# Agent restart
./scripts/restart_agents.sh

# Performance analysis
./scripts/analyze_performance.sh
```

### Log Locations
- **Coordination Logs**: `/var/log/coordination/`
- **Agent Logs**: `/var/log/agents/`
- **System Logs**: `/var/log/system/`
- **Security Logs**: `/var/log/security/`

---

**Operating Procedures Version**: v1.0.0  
**Created**: 2025-07-08  
**Framework Ticket**: FWK-007  
**Review Schedule**: Monthly  
**Last Updated**: 2025-07-08