# Multi-Agent Coordination Implementation Specifications

## Overview

This document provides detailed implementation specifications for the Multi-Agent Coordination Architecture (FWK-007), including integration points, technical requirements, and deployment procedures.

## 1. Integration with Existing Framework

### 1.1 Task Tool Subprocess Integration

#### Core Integration Points
```python
# framework/coordination/subprocess_coordinator.py
from typing import Dict, List, Optional, Any
from framework.coordination.state import SubprocessState
from framework.memory.client import MemoryClient

class SubprocessCoordinator:
    """Central coordinator for multi-agent subprocess delegation"""
    
    def __init__(self):
        self.memory_client = MemoryClient()
        self.active_subprocesses = {}
        self.conflict_resolver = ConflictResolver()
        self.resource_manager = ResourceManager()
        
    def coordinate_subprocess_delegation(self, task_context: str) -> SubprocessResult:
        """Coordinate multi-agent subprocess delegation"""
        # Analyze task complexity
        complexity = self.analyze_complexity(task_context)
        
        # Assign optimal agents for subprocess execution
        agent_assignment = self.assign_agents(complexity, task_context)
        
        # Check for conflicts
        conflicts = self.detect_conflicts(agent_assignment, task_context)
        
        if conflicts:
            resolution = self.resolve_conflicts(conflicts)
            if not resolution.successful:
                return self.escalate_subprocess(task_context, conflicts)
        
        # Create subprocess via Task tool with coordination metadata
        subprocess_config = {
            "assigned_agents": agent_assignment,
            "complexity_level": complexity.level,
            "resource_allocation": self.allocate_resources(agent_assignment),
            "estimated_duration": complexity.estimated_duration,
            "coordination_timestamp": datetime.now().isoformat()
        }
        
        return self.create_task_subprocess(task_context, subprocess_config)
```

#### Subprocess State Management
```python
# framework/coordination/subprocess_state.py
from typing import TypedDict, List, Dict, Any, Optional

class SubprocessCoordinationState(TypedDict):
    """State management for subprocess coordination"""
    # Core task information
    task: str
    context: str
    requirements: Optional[str]
    
    # Subprocess coordination fields
    active_subprocesses: List[str]
    agent_assignments: Dict[str, Dict[str, Any]]
    coordination_metadata: Dict[str, Any]
    
    # Conflict management
    conflict_history: List[Dict[str, Any]]
    escalation_status: Optional[Dict[str, Any]]
    
    # Resource management
    resource_allocations: Dict[str, Dict[str, Any]]
    resource_constraints: Optional[Dict[str, Any]]
    
    # Performance tracking
    execution_metrics: Dict[str, Any]
    subprocess_performance: Dict[str, Dict[str, Any]]
```

### 1.2 Memory System Integration

#### Coordination Memory Schema
```python
# framework/memory/coordination_schema.py
COORDINATION_MEMORY_SCHEMA = {
    "conflict_patterns": {
        "description": "Successful conflict resolution patterns",
        "fields": [
            "conflict_type",
            "involved_agents", 
            "resolution_strategy",
            "success_rate",
            "context_similarity"
        ]
    },
    "agent_performance": {
        "description": "Historical agent performance data",
        "fields": [
            "agent_type",
            "task_complexity",
            "execution_time",
            "quality_score",
            "resource_usage"
        ]
    },
    "delegation_patterns": {
        "description": "Optimal subprocess delegation configurations",
        "fields": [
            "task_pattern",
            "agent_combination",
            "delegation_strategy",
            "success_metrics",
            "optimization_opportunities"
        ]
    }
}
```

#### Memory-Driven Coordination
```python
# framework/coordination/memory_coordinator.py
class MemoryDrivenCoordinator:
    def __init__(self):
        self.memory_client = MemoryClient()
        
    def get_optimal_agent_assignment(self, task_context):
        """Get optimal agent assignment based on memory patterns"""
        # Search for similar task patterns
        similar_patterns = self.memory_client.search(
            query=f"task pattern: {task_context}",
            categories=["delegation_patterns", "agent_performance"],
            limit=10
        )
        
        if similar_patterns:
            # Analyze success patterns
            best_pattern = self.analyze_success_patterns(similar_patterns)
            return self.adapt_pattern_to_context(best_pattern, task_context)
        else:
            # Use default assignment strategy
            return self.get_default_assignment(task_context)
            
    def learn_from_execution(self, assignment, execution_result):
        """Learn from workflow execution results"""
        pattern = {
            "task_pattern": assignment.task_pattern,
            "agent_combination": assignment.agents,
            "execution_metrics": execution_result.metrics,
            "success_score": execution_result.success_score,
            "lessons_learned": execution_result.lessons
        }
        
        self.memory_client.store_pattern(
            category="delegation_patterns",
            pattern=pattern,
            tags=["coordination", "subprocess", "delegation", "optimization"]
        )
```

### 1.3 Technical Enforcement Integration

#### Coordination Policy Enforcement
```python
# framework/enforcement/coordination_policies.py
class CoordinationPolicyEnforcer:
    """Enforce coordination policies and constraints"""
    
    def __init__(self):
        self.policies = self.load_policies()
        
    def validate_agent_assignment(self, assignment):
        """Validate agent assignment against policies"""
        violations = []
        
        # Check maximum concurrent agents
        if len(assignment.agents) > self.policies.max_concurrent_agents:
            violations.append(
                PolicyViolation(
                    "max_agents_exceeded",
                    f"Assignment has {len(assignment.agents)} agents, "
                    f"maximum allowed is {self.policies.max_concurrent_agents}"
                )
            )
        
        # Check agent capability requirements
        for agent, requirements in assignment.agent_requirements.items():
            if not self.validate_agent_capabilities(agent, requirements):
                violations.append(
                    PolicyViolation(
                        "insufficient_capabilities",
                        f"Agent {agent} lacks required capabilities: {requirements}"
                    )
                )
        
        # Check resource constraints
        total_resources = self.calculate_total_resources(assignment)
        if total_resources > self.policies.resource_limit:
            violations.append(
                PolicyViolation(
                    "resource_limit_exceeded",
                    f"Assignment requires {total_resources}, "
                    f"limit is {self.policies.resource_limit}"
                )
            )
        
        return violations
        
    def enforce_dependencies(self, workflow):
        """Enforce dependency ordering constraints"""
        for step in workflow.steps:
            for dependency in step.dependencies:
                if not self.validate_dependency_order(dependency, workflow):
                    raise DependencyViolation(
                        f"Invalid dependency order: {dependency} in step {step.id}"
                    )
```

## 2. Technical Implementation Details

### 2.1 Conflict Detection System

#### Real-time Conflict Monitor
```python
# framework/coordination/conflict_detector.py
import asyncio
from typing import List, Dict, Any
from dataclasses import dataclass

@dataclass
class Conflict:
    conflict_id: str
    conflict_type: str
    involved_agents: List[str]
    severity: str  # critical, high, medium, low
    context: Dict[str, Any]
    timestamp: datetime
    
class RealTimeConflictDetector:
    def __init__(self):
        self.monitoring_interval = 5  # seconds
        self.conflict_threshold = 0.8
        self.active_monitors = {}
        
    async def start_monitoring(self):
        """Start real-time conflict monitoring"""
        while True:
            await self.check_for_conflicts()
            await asyncio.sleep(self.monitoring_interval)
            
    async def check_for_conflicts(self):
        """Check for various types of conflicts"""
        conflicts = []
        
        # Resource conflicts
        resource_conflicts = await self.detect_resource_conflicts()
        conflicts.extend(resource_conflicts)
        
        # Agent conflicts
        agent_conflicts = await self.detect_agent_conflicts()
        conflicts.extend(agent_conflicts)
        
        # Priority conflicts
        priority_conflicts = await self.detect_priority_conflicts()
        conflicts.extend(priority_conflicts)
        
        # Technical conflicts
        technical_conflicts = await self.detect_technical_conflicts()
        conflicts.extend(technical_conflicts)
        
        # Process detected conflicts
        for conflict in conflicts:
            await self.handle_conflict(conflict)
            
    async def detect_resource_conflicts(self) -> List[Conflict]:
        """Detect resource contention conflicts"""
        conflicts = []
        
        # Check file access conflicts
        file_access_map = self.get_active_file_access()
        for file_path, accessing_agents in file_access_map.items():
            if len(accessing_agents) > 1:
                conflicts.append(Conflict(
                    conflict_id=f"file_conflict_{hash(file_path)}",
                    conflict_type="resource_contention",
                    involved_agents=accessing_agents,
                    severity="medium",
                    context={"resource": file_path, "type": "file_access"},
                    timestamp=datetime.now()
                ))
                
        # Check git worktree conflicts
        worktree_conflicts = self.detect_worktree_conflicts()
        conflicts.extend(worktree_conflicts)
        
        # Check memory/compute conflicts
        compute_conflicts = self.detect_compute_conflicts()
        conflicts.extend(compute_conflicts)
        
        return conflicts
        
    async def detect_technical_conflicts(self) -> List[Conflict]:
        """Detect technical decision conflicts"""
        conflicts = []
        
        # Get recent agent decisions
        recent_decisions = self.get_recent_agent_decisions()
        
        # Check for contradictory decisions
        for decision_group in self.group_related_decisions(recent_decisions):
            if self.has_contradictions(decision_group):
                conflicts.append(Conflict(
                    conflict_id=f"technical_conflict_{decision_group.id}",
                    conflict_type="technical_disagreement",
                    involved_agents=[d.agent for d in decision_group.decisions],
                    severity=self.assess_contradiction_severity(decision_group),
                    context={
                        "decisions": [d.summary for d in decision_group.decisions],
                        "contradiction_type": self.classify_contradiction(decision_group)
                    },
                    timestamp=datetime.now()
                ))
                
        return conflicts
```

### 2.2 Conflict Resolution Engine

#### Automatic Resolution Strategies
```python
# framework/coordination/conflict_resolver.py
class ConflictResolver:
    def __init__(self):
        self.resolution_strategies = {
            "resource_contention": [
                self.queue_access_strategy,
                self.resource_splitting_strategy,
                self.priority_boost_strategy
            ],
            "technical_disagreement": [
                self.framework_pattern_strategy,
                self.architect_authority_strategy,
                self.research_consultation_strategy
            ],
            "priority_conflict": [
                self.timeline_analysis_strategy,
                self.business_impact_strategy,
                self.dependency_reordering_strategy
            ]
        }
        
    async def resolve_conflict(self, conflict: Conflict) -> ResolutionResult:
        """Resolve conflict using appropriate strategies"""
        strategies = self.resolution_strategies.get(conflict.conflict_type, [])
        
        for strategy in strategies:
            try:
                result = await strategy(conflict)
                if result.successful:
                    # Log successful resolution
                    await self.log_resolution(conflict, result)
                    return result
            except Exception as e:
                # Log strategy failure and continue
                await self.log_strategy_failure(conflict, strategy, e)
                continue
                
        # All automatic strategies failed, escalate
        return await self.escalate_conflict(conflict)
        
    async def queue_access_strategy(self, conflict: Conflict) -> ResolutionResult:
        """Resolve resource conflicts by queueing access"""
        if conflict.conflict_type != "resource_contention":
            return ResolutionResult(False, "Strategy not applicable")
            
        # Determine access order based on priority
        agents = conflict.involved_agents
        priority_order = await self.get_priority_order(agents)
        
        # Queue agents for sequential access
        await self.queue_agents_for_resource(
            conflict.context["resource"],
            priority_order
        )
        
        return ResolutionResult(
            True, 
            f"Queued agents for sequential access: {priority_order}",
            {"access_order": priority_order, "estimated_delay": self.calculate_delay(priority_order)}
        )
        
    async def framework_pattern_strategy(self, conflict: Conflict) -> ResolutionResult:
        """Resolve technical conflicts using framework patterns"""
        if conflict.conflict_type != "technical_disagreement":
            return ResolutionResult(False, "Strategy not applicable")
            
        # Look up framework patterns for this type of decision
        patterns = await self.get_framework_patterns(conflict.context)
        
        if patterns:
            # Apply the highest-confidence pattern
            best_pattern = max(patterns, key=lambda p: p.confidence)
            resolution = await self.apply_pattern(best_pattern, conflict)
            
            return ResolutionResult(
                True,
                f"Applied framework pattern: {best_pattern.name}",
                {"pattern": best_pattern, "resolution": resolution}
            )
        else:
            return ResolutionResult(False, "No applicable framework patterns found")
```

### 2.3 Resource Management System

#### Dynamic Resource Allocation
```python
# framework/coordination/resource_manager.py
class ResourceManager:
    def __init__(self):
        self.resource_pools = {
            "cpu": {"total": 100, "available": 100, "unit": "cores"},
            "memory": {"total": 64, "available": 64, "unit": "GB"},
            "storage": {"total": 1000, "available": 1000, "unit": "GB"},
            "git_worktrees": {"total": 20, "available": 20, "unit": "instances"},
            "network": {"total": 1000, "available": 1000, "unit": "Mbps"}
        }
        self.allocations = {}
        self.allocation_history = []
        
    async def allocate_resources(self, agent_id: str, requirements: Dict[str, int]) -> AllocationResult:
        """Allocate resources to an agent"""
        # Check if allocation is possible
        if not self.can_allocate(requirements):
            # Try to free up resources
            freed = await self.attempt_resource_optimization()
            if not freed or not self.can_allocate(requirements):
                return AllocationResult(
                    False, 
                    "Insufficient resources available",
                    {"required": requirements, "available": self.get_available_resources()}
                )
        
        # Perform allocation
        allocation = {
            "agent_id": agent_id,
            "resources": requirements,
            "timestamp": datetime.now(),
            "allocation_id": f"alloc_{agent_id}_{int(time.time())}"
        }
        
        # Update resource pools
        for resource_type, amount in requirements.items():
            self.resource_pools[resource_type]["available"] -= amount
            
        # Track allocation
        self.allocations[agent_id] = allocation
        self.allocation_history.append(allocation)
        
        return AllocationResult(
            True,
            f"Resources allocated to {agent_id}",
            allocation
        )
        
    async def attempt_resource_optimization(self) -> bool:
        """Attempt to optimize resource usage to free up resources"""
        optimizations_applied = False
        
        # Look for idle agents
        idle_agents = await self.find_idle_agents()
        for agent in idle_agents:
            if agent in self.allocations:
                await self.release_resources(agent)
                optimizations_applied = True
                
        # Look for over-allocated agents
        over_allocated = await self.find_over_allocated_agents()
        for agent, excess in over_allocated.items():
            await self.reduce_allocation(agent, excess)
            optimizations_applied = True
            
        # Compress resource usage
        if await self.compress_resource_usage():
            optimizations_applied = True
            
        return optimizations_applied
        
    async def predict_resource_needs(self, task_context: str) -> Dict[str, int]:
        """Predict resource needs based on task context and history"""
        # Use historical data to predict needs
        similar_tasks = await self.find_similar_tasks(task_context)
        
        if similar_tasks:
            # Calculate average resource usage for similar tasks
            avg_usage = self.calculate_average_usage(similar_tasks)
            # Add 20% buffer for safety
            return {k: int(v * 1.2) for k, v in avg_usage.items()}
        else:
            # Use default heuristics
            return self.get_default_resource_estimate(task_context)
```

### 2.4 Performance Monitoring

#### Real-time Performance Tracking
```python
# framework/coordination/performance_monitor.py
class PerformanceMonitor:
    def __init__(self):
        self.metrics_store = MetricsStore()
        self.alert_thresholds = {
            "agent_utilization": {"min": 0.6, "max": 0.95},
            "coordination_overhead": {"max": 0.05},
            "conflict_resolution_time": {"max": 1800},  # 30 minutes
            "resource_efficiency": {"min": 0.85},
            "task_completion_rate": {"min": 0.95}
        }
        
    async def collect_metrics(self):
        """Collect comprehensive performance metrics"""
        metrics = {
            "timestamp": datetime.now().isoformat(),
            "agent_metrics": await self.collect_agent_metrics(),
            "resource_metrics": await self.collect_resource_metrics(),
            "coordination_metrics": await self.collect_coordination_metrics(),
            "system_metrics": await self.collect_system_metrics()
        }
        
        # Store metrics
        await self.metrics_store.store(metrics)
        
        # Check for threshold violations
        await self.check_alert_thresholds(metrics)
        
        return metrics
        
    async def collect_agent_metrics(self):
        """Collect agent-specific performance metrics"""
        agent_metrics = {}
        
        for agent_id in self.get_active_agents():
            metrics = {
                "utilization": await self.get_agent_utilization(agent_id),
                "task_queue_length": await self.get_queue_length(agent_id),
                "average_task_duration": await self.get_avg_duration(agent_id),
                "success_rate": await self.get_success_rate(agent_id),
                "resource_usage": await self.get_resource_usage(agent_id),
                "memory_integration_latency": await self.get_memory_latency(agent_id)
            }
            agent_metrics[agent_id] = metrics
            
        return agent_metrics
        
    async def detect_performance_anomalies(self, current_metrics):
        """Detect performance anomalies and bottlenecks"""
        anomalies = []
        
        # Check agent performance
        for agent_id, metrics in current_metrics["agent_metrics"].items():
            if metrics["utilization"] < self.alert_thresholds["agent_utilization"]["min"]:
                anomalies.append({
                    "type": "underutilization",
                    "agent": agent_id,
                    "current_value": metrics["utilization"],
                    "threshold": self.alert_thresholds["agent_utilization"]["min"]
                })
                
            if metrics["utilization"] > self.alert_thresholds["agent_utilization"]["max"]:
                anomalies.append({
                    "type": "overutilization", 
                    "agent": agent_id,
                    "current_value": metrics["utilization"],
                    "threshold": self.alert_thresholds["agent_utilization"]["max"]
                })
                
        # Check coordination overhead
        coord_overhead = current_metrics["coordination_metrics"]["overhead_percentage"]
        if coord_overhead > self.alert_thresholds["coordination_overhead"]["max"]:
            anomalies.append({
                "type": "high_coordination_overhead",
                "current_value": coord_overhead,
                "threshold": self.alert_thresholds["coordination_overhead"]["max"]
            })
            
        return anomalies
```

## 3. Deployment Strategy

### 3.1 Phased Rollout Plan

#### Phase 1: Core Infrastructure (Week 1)
- Deploy basic coordination infrastructure
- Implement conflict detection system
- Set up resource management
- Basic monitoring and alerting

#### Phase 2: Advanced Features (Week 2)
- Deploy conflict resolution engine
- Implement memory integration
- Advanced resource optimization
- Performance monitoring dashboard

#### Phase 3: Full Integration (Week 3)
- Complete LangGraph workflow integration
- Deploy all coordination patterns
- Comprehensive testing
- Documentation and training

#### Phase 4: Optimization (Week 4)
- Performance tuning
- Advanced analytics
- Predictive capabilities
- Scalability improvements

### 3.2 Testing Strategy

#### Unit Testing
```python
# tests/coordination/test_conflict_detector.py
import pytest
from framework.coordination.conflict_detector import RealTimeConflictDetector

class TestConflictDetector:
    def setUp(self):
        self.detector = RealTimeConflictDetector()
        
    async def test_resource_conflict_detection(self):
        """Test resource conflict detection"""
        # Setup test scenario
        self.detector.set_active_file_access({
            "/test/file.py": ["agent1", "agent2"]
        })
        
        conflicts = await self.detector.detect_resource_conflicts()
        
        assert len(conflicts) == 1
        assert conflicts[0].conflict_type == "resource_contention"
        assert "agent1" in conflicts[0].involved_agents
        assert "agent2" in conflicts[0].involved_agents
        
    async def test_technical_conflict_detection(self):
        """Test technical disagreement detection"""
        # Setup contradictory decisions
        decisions = [
            {"agent": "architect", "decision": "use_microservices"},
            {"agent": "engineer", "decision": "use_monolith"}
        ]
        
        self.detector.set_recent_decisions(decisions)
        conflicts = await self.detector.detect_technical_conflicts()
        
        assert len(conflicts) == 1
        assert conflicts[0].conflict_type == "technical_disagreement"
```

#### Integration Testing
```python
# tests/coordination/test_integration.py
class TestCoordinationIntegration:
    async def test_full_subprocess_coordination(self):
        """Test complete subprocess coordination"""
        # Create test task
        task_context = self.create_test_task_context()
        
        # Execute with coordination
        coordinator = SubprocessCoordinator()
        result = await coordinator.coordinate_subprocess_delegation(task_context)
        
        # Verify coordination metadata
        assert "coordination_metadata" in result.config
        assert "assigned_agents" in result.config["coordination_metadata"]
        assert "resource_allocation" in result.config["coordination_metadata"]
        
    async def test_conflict_resolution_integration(self):
        """Test conflict resolution in subprocess delegation"""
        # Create conflicting scenario
        task_context = self.create_conflicting_task_context()
        
        coordinator = SubprocessCoordinator()
        result = await coordinator.coordinate_subprocess_delegation(task_context)
        
        # Verify conflicts were resolved
        assert result.escalation_status is None  # No escalation needed
        assert len(result.conflict_history) > 0  # Conflicts were detected and resolved
```

### 3.3 Monitoring and Observability

#### Monitoring Stack
- **Metrics Collection**: Prometheus-compatible metrics
- **Visualization**: Grafana dashboards
- **Alerting**: PagerDuty integration
- **Logging**: Structured logging with correlation IDs
- **Tracing**: Distributed tracing for coordination flows

#### Key Dashboards
1. **Agent Performance Dashboard**
   - Agent utilization rates
   - Task completion metrics
   - Resource usage patterns
   - Quality metrics

2. **Coordination Health Dashboard**
   - Active conflicts
   - Resolution times
   - Escalation rates
   - System health indicators

3. **Resource Management Dashboard**
   - Resource utilization
   - Allocation efficiency
   - Prediction accuracy
   - Optimization opportunities

## 4. Migration and Rollback

### 4.1 Migration Strategy
- Gradual rollout with feature flags
- Backward compatibility maintenance
- Data migration for existing workflows
- Rollback procedures for each phase

### 4.2 Risk Mitigation
- Comprehensive testing in staging environment
- Canary deployments for production
- Real-time monitoring and alerting
- Automated rollback triggers

### 4.3 Success Criteria
- All 42 tickets can be coordinated simultaneously
- Conflict resolution rate >95%
- Agent utilization >85%
- Coordination overhead <5%
- Zero data loss during migration

---

## Implementation Checklist

### Core Infrastructure
- [x] Deploy coordination database and storage
- [x] Implement basic agent communication
- [x] Set up resource tracking
- [x] Deploy monitoring infrastructure

### Coordination Features
- [x] Implement conflict detection
- [x] Deploy resolution engine
- [x] Set up escalation procedures
- [x] Implement resource management

### Integration
- [x] Integrate with Task tool subprocess delegation
- [x] Connect to memory system
- [x] Deploy technical enforcement
- [x] Set up performance monitoring

### Testing and Validation
- [ ] Complete unit test suite
- [ ] Run integration tests
- [ ] Perform load testing
- [ ] Validate security measures

### Documentation and Training
- [ ] Complete technical documentation
- [ ] Create operator guides
- [ ] Develop troubleshooting procedures
- [ ] Conduct team training

---

**Implementation Version**: v1.0.0  
**Created**: 2025-07-08  
**Framework Ticket**: FWK-007  
**Dependencies**: FWK-003 (Technical Enforcement)  
**Status**: Implementation Complete - Pure Subprocess Delegation Operational