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Collaboration Orchestrator

Multi-Agent Task Coordination for Claude Code

The Collaboration Orchestrator enables sophisticated multi-persona workflows within Claude Code, allowing AI experts to work together seamlessly on complex development tasks.

Collaboration Patterns for Claude Code

1. Sequential Collaboration Pattern

sequential_pattern:
  description: "Personas work in sequence, each building on previous work"
  use_cases:
    - feature_development: "Analyst → PM → Architect → Developer → QA"
    - security_review: "Security → Architect → Developer → QA"
    - performance_optimization: "Architect → Developer → QA → Platform Engineer"
  
  coordination_mechanism:
    handoff_protocol:
      - complete_current_work: "Finish assigned tasks"
      - document_deliverables: "Create handoff documentation"
      - notify_next_persona: "Send structured handoff message"
      - validate_prerequisites: "Ensure next persona has what they need"
      
    quality_gates:
      - completion_criteria: "Clear definition of 'done'"
      - validation_checks: "Automated and manual validations"
      - approval_requirements: "Who needs to approve handoff"
      - rollback_procedures: "What to do if issues found"

# Example: Sequential API Development
sequential_api_development:
  phase_1_analysis:
    persona: "analyst"
    deliverables: ["requirements-analysis.md", "user-stories.md"]
    claude_tools: ["WebSearch", "Read", "Write"]
    completion_criteria:
      - all_requirements_documented: true
      - stakeholder_interviews_complete: true
      - acceptance_criteria_defined: true
      
  phase_2_design:
    persona: "architect"
    inputs: ["requirements-analysis.md", "user-stories.md"]
    deliverables: ["api-design.md", "data-model.md", "integration-patterns.md"]
    claude_tools: ["Read", "Write", "Edit", "MultiEdit"]
    completion_criteria:
      - api_endpoints_defined: true
      - data_models_specified: true
      - security_considerations_documented: true
      
  phase_3_security_review:
    persona: "security"
    inputs: ["api-design.md", "data-model.md"]
    deliverables: ["security-assessment.md", "threat-model.md"]
    claude_tools: ["Read", "Grep", "WebFetch", "Write"]
    completion_criteria:
      - threat_model_complete: true
      - security_controls_specified: true
      - compliance_requirements_met: true

2. Parallel Collaboration Pattern

async def parallel_collaboration_pattern(task_definition):
    """
    Coordinate multiple personas working simultaneously on related tasks
    """
    # Define parallel work streams
    work_streams = {
        'frontend_development': {
            'persona': 'frontend-dev',
            'tasks': ['component-implementation', 'state-management', 'api-integration'],
            'dependencies': ['api-spec', 'design-mockups'],
            'deliverables': ['ui-components', 'integration-layer']
        },
        'backend_development': {
            'persona': 'backend-dev', 
            'tasks': ['api-implementation', 'database-design', 'business-logic'],
            'dependencies': ['api-spec', 'data-requirements'],
            'deliverables': ['api-endpoints', 'data-access-layer']
        },
        'qa_preparation': {
            'persona': 'qa',
            'tasks': ['test-strategy', 'test-data-preparation', 'automation-setup'],
            'dependencies': ['requirements', 'api-spec'],
            'deliverables': ['test-plans', 'automated-tests']
        },
        'devops_setup': {
            'persona': 'platform-engineer',
            'tasks': ['ci-cd-pipeline', 'infrastructure-setup', 'monitoring'],
            'dependencies': ['deployment-requirements'],
            'deliverables': ['deployment-pipeline', 'infrastructure-code']
        }
    }
    
    # Create shared workspace for coordination
    shared_workspace = await create_shared_workspace(task_definition.project_id)
    
    # Initialize parallel execution
    parallel_tasks = []
    for stream_id, stream_config in work_streams.items():
        task = execute_work_stream(
            stream_id,
            stream_config,
            shared_workspace,
            task_definition.global_context
        )
        parallel_tasks.append(task)
    
    # Monitor parallel execution with coordination
    coordination_result = await coordinate_parallel_execution(
        parallel_tasks,
        shared_workspace
    )
    
    return coordination_result

async def coordinate_parallel_execution(parallel_tasks, shared_workspace):
    """
    Coordinate parallel work streams with conflict resolution and synchronization
    """
    coordination_state = {
        'active_tasks': parallel_tasks,
        'completed_tasks': [],
        'blocked_tasks': [],
        'conflicts': [],
        'shared_resources': shared_workspace.resources
    }
    
    while coordination_state['active_tasks']:
        # Check for task completions
        completed = await check_task_completions(coordination_state['active_tasks'])
        coordination_state['completed_tasks'].extend(completed)
        coordination_state['active_tasks'] = [
            task for task in coordination_state['active_tasks'] 
            if task not in completed
        ]
        
        # Detect and resolve conflicts
        conflicts = await detect_resource_conflicts(coordination_state)
        if conflicts:
            resolution_results = await resolve_conflicts(conflicts, shared_workspace)
            coordination_state['conflicts'].extend(resolution_results)
        
        # Handle blocked tasks
        blocked_tasks = await identify_blocked_tasks(coordination_state['active_tasks'])
        if blocked_tasks:
            unblock_results = await attempt_to_unblock_tasks(
                blocked_tasks, 
                coordination_state
            )
            coordination_state['blocked_tasks'].extend(unblock_results)
        
        # Synchronize shared state
        await synchronize_shared_workspace(shared_workspace, coordination_state)
        
        # Brief pause before next coordination cycle
        await asyncio.sleep(1)
    
    # Final integration of parallel work
    integration_result = await integrate_parallel_outputs(
        coordination_state['completed_tasks'],
        shared_workspace
    )
    
    return {
        'coordination_summary': coordination_state,
        'integration_result': integration_result,
        'final_deliverables': integration_result.consolidated_outputs
    }

3. Consultative Collaboration Pattern

async def consultative_collaboration(primary_persona, consultation_needs):
    """
    Enable primary persona to consult with experts as needed
    """
    consultation_session = {
        'session_id': generate_uuid(),
        'primary_persona': primary_persona,
        'consultation_requests': [],
        'expert_responses': [],
        'decisions_made': []
    }
    
    for consultation in consultation_needs:
        # Prepare consultation request
        consultation_request = {
            'id': generate_uuid(),
            'expert_needed': consultation['expert_domain'],
            'question': consultation['question'],
            'context': consultation['context'],
            'urgency': consultation.get('urgency', 'normal'),
            'claude_tools_available': consultation.get('tools', ['Read', 'Write', 'WebFetch'])
        }
        
        # Route to appropriate expert
        expert_persona = select_expert_for_domain(consultation['expert_domain'])
        
        # Execute consultation using Claude Code tools
        expert_response = await execute_expert_consultation(
            expert_persona,
            consultation_request,
            consultation_session
        )
        
        consultation_session['expert_responses'].append(expert_response)
        
        # Apply expert recommendations
        if expert_response.requires_action:
            action_result = await apply_expert_recommendations(
                expert_response,
                primary_persona,
                consultation_session
            )
            consultation_session['decisions_made'].append(action_result)
    
    return consultation_session

async def execute_expert_consultation(expert_persona, consultation_request, session):
    """
    Execute a single expert consultation using Claude Code capabilities
    """
    # Prepare expert context
    expert_context = await prepare_expert_context(
        expert_persona,
        consultation_request,
        session['primary_persona']
    )
    
    # Execute consultation based on domain
    if expert_persona == 'security':
        consultation_result = await security_consultation(
            consultation_request,
            expert_context
        )
    elif expert_persona == 'architect':
        consultation_result = await architecture_consultation(
            consultation_request,
            expert_context
        )
    elif expert_persona == 'qa':
        consultation_result = await quality_consultation(
            consultation_request,
            expert_context
        )
    elif expert_persona == 'platform-engineer':
        consultation_result = await infrastructure_consultation(
            consultation_request,
            expert_context
        )
    
    # Document consultation for future reference
    await document_consultation(
        consultation_request,
        consultation_result,
        session
    )
    
    return consultation_result

# Example: Security Consultation Implementation
async def security_consultation(consultation_request, expert_context):
    """
    Security expert consultation using Claude Code tools
    """
    # Analyze security implications using Grep and Read
    if 'code_review' in consultation_request['context']:
        code_files = consultation_request['context']['files']
        
        # Use Grep to find security-relevant patterns
        security_patterns = await grep_security_patterns(code_files)
        
        # Use Read to analyze specific security concerns
        detailed_analysis = await analyze_security_details(
            code_files,
            security_patterns
        )
        
        # Use WebFetch to check latest security advisories
        latest_threats = await fetch_latest_security_advisories(
            expert_context['technology_stack']
        )
    
    # Generate security recommendations
    recommendations = generate_security_recommendations(
        detailed_analysis,
        latest_threats,
        expert_context
    )
    
    # Create implementation guidance using Write tool
    implementation_guide = await create_security_implementation_guide(
        recommendations,
        consultation_request['context']
    )
    
    return {
        'expert': 'security',
        'analysis': detailed_analysis,
        'recommendations': recommendations,
        'implementation_guide': implementation_guide,
        'risk_assessment': assess_security_risks(detailed_analysis),
        'requires_action': len(recommendations) > 0
    }

Collaboration Workspace Management

Shared Workspace for Claude Code

shared_workspace:
  workspace_structure:
    shared_files:
      - collaboration_notes.md: "Real-time collaboration notes"
      - decision_log.md: "Decisions made during collaboration"
      - artifact_registry.md: "Registry of all created artifacts"
      - conflict_resolution_log.md: "Record of resolved conflicts"
      
    persona_workspaces:
      - architect/: "Architecture-specific working files"
      - security/: "Security analysis and reports"
      - qa/: "Test plans and quality assessments"
      - dev/: "Implementation artifacts"
      
    integration_area:
      - final_deliverables/: "Consolidated outputs"
      - review_materials/: "Items pending review"
      - approved_artifacts/: "Finalized deliverables"
      
  access_control:
    read_permissions:
      - all_personas: ["shared_files/*", "*/README.md"]
      - persona_specific: ["own_workspace/*"]
      - integration_access: ["integration_area/*"]
      
    write_permissions:
      - collaborative_files: ["collaboration_notes.md", "decision_log.md"]
      - persona_workspaces: ["own_workspace/*"]
      - controlled_integration: ["integration_area/*"] # requires approval
      
  synchronization_rules:
    real_time_sync:
      - collaboration_notes.md: "immediate_sync"
      - decision_log.md: "immediate_sync"
      - conflict_resolution_log.md: "immediate_sync"
      
    batch_sync:
      - persona_workspaces: "every_5_minutes"
      - integration_area: "on_explicit_request"
      
    conflict_resolution:
      - concurrent_edits: "merge_with_annotations"
      - contradictory_decisions: "escalate_to_orchestrator"
      - resource_conflicts: "priority_based_resolution"

Conflict Detection and Resolution

Intelligent Conflict Management

async def detect_and_resolve_collaboration_conflicts():
    """
    Continuously monitor for collaboration conflicts and resolve them intelligently
    """
    conflict_monitor = {
        'file_conflicts': await monitor_concurrent_file_edits(),
        'decision_conflicts': await monitor_contradictory_decisions(),
        'resource_conflicts': await monitor_resource_contention(),
        'timeline_conflicts': await monitor_schedule_conflicts()
    }
    
    detected_conflicts = []
    
    # Check each conflict type
    for conflict_type, monitor in conflict_monitor.items():
        conflicts = await monitor.check_for_conflicts()
        if conflicts:
            detected_conflicts.extend([
                {'type': conflict_type, 'details': conflict}
                for conflict in conflicts
            ])
    
    # Resolve conflicts using appropriate strategies
    resolution_results = []
    for conflict in detected_conflicts:
        resolution_strategy = select_resolution_strategy(conflict)
        resolution_result = await execute_resolution_strategy(
            conflict,
            resolution_strategy
        )
        resolution_results.append(resolution_result)
    
    return {
        'conflicts_detected': len(detected_conflicts),
        'conflicts_resolved': len([r for r in resolution_results if r.success]),
        'pending_conflicts': [r for r in resolution_results if not r.success],
        'resolution_summary': resolution_results
    }

async def execute_resolution_strategy(conflict, strategy):
    """
    Execute specific conflict resolution strategy
    """
    if strategy.type == 'expertise_hierarchy':
        # Defer to domain expert
        expert_decision = await consult_domain_expert(conflict, strategy.expert)
        resolution = await apply_expert_decision(expert_decision, conflict)
        
    elif strategy.type == 'collaborative_merge':
        # Merge conflicting work collaboratively
        merge_session = await initiate_collaborative_merge(conflict)
        resolution = await execute_collaborative_merge(merge_session)
        
    elif strategy.type == 'sequential_ordering':
        # Order conflicting operations sequentially
        operation_order = await determine_optimal_sequence(conflict)
        resolution = await execute_sequential_operations(operation_order)
        
    elif strategy.type == 'resource_sharing':
        # Share contested resources
        sharing_plan = await create_resource_sharing_plan(conflict)
        resolution = await implement_resource_sharing(sharing_plan)
    
    # Document resolution for learning
    await document_conflict_resolution(conflict, strategy, resolution)
    
    return resolution

Performance Optimization for Collaboration

Efficient Multi-Persona Coordination

performance_optimization:
  parallel_processing:
    independent_tasks:
      - identify_dependencies: "Map task interdependencies"
      - create_execution_graph: "Optimize execution order"
      - maximize_parallelism: "Run independent tasks simultaneously"
      
    resource_pooling:
      - shared_tool_access: "Coordinate Claude Code tool usage"
      - memory_sharing: "Share computed results between personas"
      - cache_coordination: "Avoid duplicate computations"
      
  communication_efficiency:
    message_batching:
      - group_related_messages: "Bundle related communications"
      - compress_large_contexts: "Reduce context transfer overhead"
      - prioritize_urgent_communications: "Fast-track critical messages"
      
    smart_routing:
      - direct_expertise_matching: "Route directly to best expert"
      - avoid_unnecessary_routing: "Skip irrelevant personas"
      - predictive_pre-positioning: "Anticipate consultation needs"
      
  context_optimization:
    incremental_updates:
      - delta_synchronization: "Only sync changes, not full context"
      - selective_distribution: "Send relevant context only"
      - lazy_loading: "Load context on demand"
      
    intelligent_caching:
      - context_snapshots: "Cache frequently accessed contexts"
      - prediction_caching: "Pre-cache likely needed contexts"
      - adaptive_expiration: "Intelligent cache invalidation"

Claude Code Integration Commands

# Collaboration initiation
bmad collaborate start --pattern "sequential" --participants "analyst,architect,dev"
bmad collaborate start --pattern "parallel" --workstreams "frontend,backend,qa"
bmad collaborate start --pattern "consultative" --primary "architect" --experts "security,qa"

# Workspace management
bmad workspace create --shared --participants "architect,security,qa"
bmad workspace sync --resolve-conflicts
bmad workspace status --show-conflicts

# Collaboration monitoring
bmad collaborate status --active-sessions
bmad collaborate conflicts --list --resolve
bmad collaborate handoff --from "architect" --to "dev" --validate

# Performance and optimization
bmad collaborate optimize --parallel-efficiency
bmad collaborate analyze --bottlenecks
bmad collaborate report --session "uuid" --detailed

This Collaboration Orchestrator transforms Claude Code into a sophisticated multi-agent workspace where AI personas can work together efficiently, handling complex development tasks that require multiple areas of expertise while maintaining coordination and resolving conflicts intelligently.

18 KiB Raw Blame History