# Agents & Delegation Comprehensive Guide - Claude PM Framework

## Overview

This comprehensive guide covers all agent management and delegation aspects of the Claude PM Framework v4.5.1, including agent types, delegation strategies, coordination protocols, and multi-agent orchestration for efficient project management.

## Table of Contents

1. [Agent Architecture](#agent-architecture)
2. [Agent Types and Specializations](#agent-types-and-specializations)
3. [Delegation Framework](#delegation-framework)
4. [Multi-Agent Orchestration](#multi-agent-orchestration)
5. [Agent Display Names and Identification](#agent-display-names-and-identification)
6. [User-Defined Agents Strategy](#user-defined-agents-strategy)
7. [First Delegation Procedures](#first-delegation-procedures)
8. [Best Practices](#best-practices)
9. [Troubleshooting](#troubleshooting)

## Agent Architecture

### Three-Tier Agent Hierarchy

The Claude PM Framework implements a sophisticated three-tier agent hierarchy that ensures optimal task delegation and system organization:

#### 1. Project Agents (Highest Priority)
**Location**: `$PROJECT/.claude-pm/agents/project-specific/`
- Project-specific implementations and overrides
- Highest precedence for project context
- Custom behavior for specific project requirements

#### 2. User Agents (Mid Priority)
**Location**: `~/.claude-pm/agents/user-defined/`
- User-specific customizations across all projects
- Mid-priority, can override system defaults
- Personal workflow optimizations

#### 3. System Agents (Fallback)
**Location**: `/framework/claude_pm/agents/`
- Core framework functionality
- Lowest precedence but always available as fallback
- Foundation agents for all operations

### Agent Loading Rules

```python
def load_agent_with_hierarchy(agent_type: str) -> Agent:
    """Load agent following three-tier hierarchy with automatic fallback."""
    
    # Priority 1: Project-specific agent
    project_agent_path = f"{PROJECT_ROOT}/.claude-pm/agents/project-specific/{agent_type}.py"
    if os.path.exists(project_agent_path):
        return load_agent(project_agent_path)
    
    # Priority 2: User-defined agent
    user_agent_path = f"{HOME}/.claude-pm/agents/user-defined/{agent_type}.py"
    if os.path.exists(user_agent_path):
        return load_agent(user_agent_path)
    
    # Priority 3: System agent (fallback)
    system_agent_path = f"{FRAMEWORK_PATH}/claude_pm/agents/{agent_type}.py"
    return load_agent(system_agent_path)  # Always available
```

## Agent Types and Specializations

### Core Production Agents

#### Engineer Agent
**Authority**: Source code implementation  
**Allocation**: MULTIPLE allowed (git worktrees support)  
**Responsibilities**:
- Source code development and implementation
- Code refactoring and optimization
- Feature implementation
- Bug fixes and patches

```python
# Engineer agent delegation example
engineer_task = {
    "agent_type": "engineer",
    "task": "Implement user authentication system",
    "authority": "source_code_only",
    "context": {
        "requirements": "JWT tokens with refresh capability",
        "technologies": ["FastAPI", "SQLAlchemy", "Redis"],
        "testing_required": True
    }
}
```

#### Ops Agent
**Authority**: Configuration and deployment  
**Allocation**: ONE per project  
**Responsibilities**:
- Deployment pipeline management
- Configuration management
- Infrastructure as code
- Push operations and CI/CD

```python
# Ops agent delegation example
ops_task = {
    "agent_type": "ops",
    "task": "Deploy application to staging environment",
    "authority": "configuration_deployment",
    "context": {
        "environment": "staging",
        "deployment_strategy": "blue_green",
        "rollback_plan": "automated"
    }
}
```

#### QA Agent
**Authority**: Testing and validation  
**Allocation**: ONE per project  
**Responsibilities**:
- Test planning and execution
- Quality assurance procedures
- Validation protocols
- Test automation

```python
# QA agent delegation example
qa_task = {
    "agent_type": "qa",
    "task": "Create comprehensive test suite for authentication",
    "authority": "tests_validation",
    "context": {
        "test_types": ["unit", "integration", "e2e"],
        "coverage_target": "90%",
        "frameworks": ["pytest", "selenium"]
    }
}
```

#### Research Agent
**Authority**: Documentation and research  
**Allocation**: ONE per project  
**Responsibilities**:
- Technical documentation
- Best practices research
- Knowledge management
- Documentation maintenance

#### Architect Agent
**Authority**: System design and scaffolding  
**Allocation**: ONE per project  
**Responsibilities**:
- System architecture design
- API specifications
- Technology selection
- Design patterns implementation

### Specialized Support Agents

#### Security Agent
**Authority**: Security analysis and compliance  
**Allocation**: ONE per project  
**Responsibilities**:
- Security audits and analysis
- Vulnerability assessments
- Compliance verification
- Security best practices

#### Performance Agent
**Authority**: Performance optimization  
**Allocation**: ONE per project  
**Responsibilities**:
- Performance monitoring
- Optimization strategies
- Resource usage analysis
- Scalability planning

#### Documentation Agent
**Authority**: Technical writing and documentation  
**Allocation**: ONE per project  
**Responsibilities**:
- Technical documentation creation
- User guides and tutorials
- API documentation
- Knowledge base management

#### Integration Agent
**Authority**: System integration and coordination  
**Allocation**: ONE per project  
**Responsibilities**:
- System integration planning
- Service coordination
- Data flow management
- Integration testing

#### Code Review Engineer
**Authority**: Code review processes  
**Allocation**: MULTIPLE allowed  
**Responsibilities**:
- Code quality reviews
- Best practices enforcement
- Security code review
- Architecture validation

## Delegation Framework

### Pattern Recognition (Immediate Delegation)

The framework uses intelligent pattern recognition to automatically delegate tasks based on user request patterns:

| User Request Pattern | Target Agent | Action Required |
|---------------------|--------------|-----------------|
| "push" or "deploy" | Ops Agent | Comprehensive deployment pipeline |
| "test" or "validate" | QA Agent | Testing coordination |
| "security" or "audit" | Security Agent | Security analysis |
| "performance" or "optimize" | Performance Agent | Performance optimization |
| "document" or "write docs" | Research Agent | Documentation creation |
| "architecture" or "design" | Architect Agent | System design |
| "organize" or "restructure" | Code Organizer Agent | File structure optimization |
| "review" or "check code" | Code Review Engineer | Code review process |

### Agent Coordination Matrix

#### Core Production Agents Coordination

| Agent | Authority | Allocation | Escalation Path |
|-------|-----------|------------|-----------------|
| **Engineer** | Source code only | MULTIPLE (git worktrees) | Architect → PM |
| **Ops** | Configuration/deployment | ONE per project | Engineer → PM |
| **QA** | Tests/validation | ONE per project | Engineer → PM |
| **Research** | Documentation | ONE per project | Architect → PM |
| **Architect** | Scaffolding/APIs | ONE per project | PM → CTO |

#### Specialized Support Agents Coordination

| Agent | Authority | Allocation | Escalation Path |
|-------|-----------|------------|-----------------|
| **Security** | Security/compliance | ONE per project | Architect → PM |
| **Performance** | Optimization/monitoring | ONE per project | Engineer → PM |
| **Documentation** | Technical writing | ONE per project | Research → PM |
| **Integration** | System integration | ONE per project | Architect → PM |
| **Code Review** | Code review | MULTIPLE allowed | Engineer → PM |

### Delegation Decision Tree

```python
def delegate_task(task_description: str, context: Dict[str, Any]) -> AgentDelegation:
    """
    Intelligent task delegation based on task analysis and context.
    """
    
    # 1. Pattern Recognition Phase
    patterns = analyze_task_patterns(task_description)
    
    if "security" in patterns or "audit" in patterns:
        return create_delegation("security", task_description, context)
    
    if "test" in patterns or "qa" in patterns:
        return create_delegation("qa", task_description, context)
    
    if "deploy" in patterns or "push" in patterns:
        return create_delegation("ops", task_description, context)
    
    if "document" in patterns or "write" in patterns:
        return create_delegation("research", task_description, context)
    
    # 2. Complexity Analysis Phase
    complexity = analyze_task_complexity(task_description)
    
    if complexity.requires_architecture:
        return create_delegation("architect", task_description, context)
    
    if complexity.requires_code_changes:
        return create_delegation("engineer", task_description, context)
    
    # 3. Default delegation to PM for complex coordination
    return create_pm_coordination_task(task_description, context)
```

## Multi-Agent Orchestration

### Orchestration Patterns

#### Sequential Delegation Pattern
For tasks requiring ordered execution:

```python
async def sequential_delegation_pattern(task_chain: List[AgentTask]) -> OrchestrationResult:
    """Execute tasks in sequence with dependency management."""
    
    results = []
    context = {}
    
    for task in task_chain:
        # Pass context from previous tasks
        task.context.update(context)
        
        # Execute task with appropriate agent
        result = await delegate_to_agent(task.agent_type, task)
        results.append(result)
        
        # Update context for next task
        context.update(result.output_context)
        
        # Handle failures
        if not result.success:
            return OrchestrationResult(
                success=False,
                error=f"Task failed at step {len(results)}: {result.error}",
                partial_results=results
            )
    
    return OrchestrationResult(success=True, results=results)
```

#### Parallel Delegation Pattern
For independent tasks that can run concurrently:

```python
async def parallel_delegation_pattern(tasks: List[AgentTask]) -> OrchestrationResult:
    """Execute multiple independent tasks in parallel."""
    
    # Create delegation coroutines
    delegations = [
        delegate_to_agent(task.agent_type, task) 
        for task in tasks
    ]
    
    # Execute all tasks concurrently
    results = await asyncio.gather(*delegations, return_exceptions=True)
    
    # Process results and handle exceptions
    successful_results = []
    errors = []
    
    for i, result in enumerate(results):
        if isinstance(result, Exception):
            errors.append(f"Task {i} failed: {str(result)}")
        else:
            successful_results.append(result)
    
    return OrchestrationResult(
        success=len(errors) == 0,
        results=successful_results,
        errors=errors
    )
```

#### Hierarchical Delegation Pattern
For complex tasks requiring multi-level delegation:

```python
async def hierarchical_delegation_pattern(master_task: MasterTask) -> OrchestrationResult:
    """Execute complex task with hierarchical agent coordination."""
    
    # 1. Architect creates high-level plan
    architecture_plan = await delegate_to_agent("architect", {
        "task": "Create implementation plan",
        "requirements": master_task.requirements,
        "constraints": master_task.constraints
    })
    
    if not architecture_plan.success:
        return OrchestrationResult(success=False, error="Architecture planning failed")
    
    # 2. Break down into subtasks
    subtasks = architecture_plan.subtasks
    
    # 3. Delegate subtasks to appropriate agents
    delegation_results = []
    
    for subtask in subtasks:
        agent_type = determine_agent_for_subtask(subtask)
        result = await delegate_to_agent(agent_type, subtask)
        delegation_results.append(result)
    
    # 4. QA validation of overall result
    qa_validation = await delegate_to_agent("qa", {
        "task": "Validate implementation against requirements",
        "implementation_results": delegation_results,
        "original_requirements": master_task.requirements
    })
    
    return OrchestrationResult(
        success=qa_validation.success,
        architecture_plan=architecture_plan,
        implementation_results=delegation_results,
        validation_result=qa_validation
    )
```

## Agent Display Names and Identification

### Agent Display Name System

The framework implements a systematic agent display name system for clear identification and professional presentation:

#### Core Agent Display Names

```python
AGENT_DISPLAY_NAMES = {
    # Core Production Agents
    "engineer": "Engineer Agent",
    "ops": "Ops Agent", 
    "qa": "QA Agent",
    "research": "Research Agent",
    "architect": "Architect Agent",
    
    # Specialized Support Agents
    "security": "Security Agent",
    "performance": "Performance Agent",
    "documentation": "Documentation Agent",
    "integration": "Integration Agent",
    "code_review": "Code Review Engineer",
    
    # Extended Ecosystem Agents
    "devops": "DevOps Agent",
    "product": "Product Agent",
    "design": "Design Agent",
    "data": "Data Agent",
    "mobile": "Mobile Agent",
    "frontend": "Frontend Agent",
    "backend": "Backend Agent",
    "api": "API Agent",
    "database": "Database Agent",
    "cloud": "Cloud Agent",
    "monitoring": "Monitoring Agent"
}
```

#### Dynamic Display Name Resolution

```python
def get_agent_display_name(agent_type: str) -> str:
    """
    Get the professional display name for an agent type.
    Supports dynamic resolution and fallback naming.
    """
    
    # 1. Check predefined display names
    if agent_type in AGENT_DISPLAY_NAMES:
        return AGENT_DISPLAY_NAMES[agent_type]
    
    # 2. Generate professional name for custom agents
    if "_" in agent_type:
        words = agent_type.split("_")
        return " ".join(word.capitalize() for word in words) + " Agent"
    
    # 3. Fallback for simple names
    return agent_type.capitalize() + " Agent"

# Examples
print(get_agent_display_name("engineer"))        # "Engineer Agent"
print(get_agent_display_name("code_review"))     # "Code Review Engineer"
print(get_agent_display_name("custom_task"))     # "Custom Task Agent"
print(get_agent_display_name("ml"))              # "Ml Agent"
```

#### Usage in TodoWrite Integration

The agent display names are automatically integrated with TodoWrite for professional task presentation:

```python
def create_agent_prefixed_todo(agent_type: str, task_description: str) -> str:
    """Create todo item with professional agent name prefix."""
    
    display_name = get_agent_display_name(agent_type)
    return f"{display_name}: {task_description}"

# Examples of automatic prefixing
todos = [
    create_agent_prefixed_todo("research", "Research implementation approach"),
    create_agent_prefixed_todo("documentation", "Document new feature requirements"),
    create_agent_prefixed_todo("qa", "Create comprehensive test suite"),
    create_agent_prefixed_todo("security", "Perform security audit"),
    create_agent_prefixed_todo("performance", "Optimize database queries")
]

# Results:
# "Research Agent: Research implementation approach"
# "Documentation Agent: Document new feature requirements"
# "QA Agent: Create comprehensive test suite"
# "Security Agent: Perform security audit"
# "Performance Agent: Optimize database queries"
```

## User-Defined Agents Strategy

### Creating Custom Agents

The framework supports user-defined agents through a sophisticated strategy that maintains consistency while allowing customization:

#### Agent Template Structure

```python
# ~/.claude-pm/agents/user-defined/custom_agent.py

from claude_pm.core.agent_base import AgentBase
from claude_pm.core.task_context import TaskContext
from claude_pm.core.agent_result import AgentResult

class CustomAgent(AgentBase):
    """
    User-defined custom agent for specialized tasks.
    """
    
    def __init__(self):
        super().__init__(
            agent_type="custom",
            display_name="Custom Agent",
            authority_level="specialized",
            allocation_strategy="one_per_project"
        )
    
    async def execute_task(self, context: TaskContext) -> AgentResult:
        """Execute the assigned task with custom logic."""
        
        try:
            # Custom implementation logic
            result = await self._perform_custom_operation(context)
            
            return AgentResult(
                success=True,
                output=result,
                metrics=self._collect_metrics(),
                recommendations=self._generate_recommendations(result)
            )
            
        except Exception as e:
            return AgentResult(
                success=False,
                error=str(e),
                diagnostics=self._collect_diagnostics()
            )
    
    async def _perform_custom_operation(self, context: TaskContext):
        """Implement custom agent logic here."""
        # Your custom implementation
        pass
    
    def _collect_metrics(self) -> Dict[str, Any]:
        """Collect performance and execution metrics."""
        return {
            "execution_time": self.execution_time,
            "resource_usage": self.resource_usage,
            "success_rate": self.success_rate
        }
    
    def _generate_recommendations(self, result) -> List[str]:
        """Generate recommendations based on execution results."""
        return [
            "Consider optimizing performance for large datasets",
            "Implement caching for repeated operations",
            "Add monitoring for production deployment"
        ]
```

#### Agent Registration

```python
# Register custom agent with the framework
from claude_pm.services.agent_registry import AgentRegistry

def register_custom_agents():
    """Register all user-defined agents with the framework."""
    
    registry = AgentRegistry()
    
    # Register custom agent
    registry.register_agent(
        agent_type="custom",
        agent_class=CustomAgent,
        priority="user_defined",
        capabilities=["specialized_processing", "data_analysis"],
        dependencies=["database", "cache"]
    )
    
    # Register with display name
    registry.register_display_name("custom", "Custom Agent")
    
    return registry
```

### Agent Capability System

#### Capability Declaration

```python
class SpecializedAgent(AgentBase):
    """Agent with declared capabilities."""
    
    capabilities = {
        "data_processing": {
            "level": "expert",
            "formats": ["json", "csv", "xml"],
            "max_size": "1GB"
        },
        "api_integration": {
            "level": "intermediate", 
            "protocols": ["REST", "GraphQL"],
            "authentication": ["OAuth2", "JWT"]
        },
        "monitoring": {
            "level": "basic",
            "metrics": ["performance", "errors"],
            "alerts": ["email", "slack"]
        }
    }
    
    def get_capability_level(self, capability: str) -> str:
        """Get agent's capability level for specific function."""
        return self.capabilities.get(capability, {}).get("level", "none")
```

#### Capability-Based Delegation

```python
def select_agent_by_capability(required_capability: str, minimum_level: str) -> str:
    """Select best agent based on required capability."""
    
    registry = AgentRegistry()
    suitable_agents = []
    
    for agent_type, agent_info in registry.get_all_agents().items():
        agent_capability = agent_info.get_capability_level(required_capability)
        
        if capability_meets_requirement(agent_capability, minimum_level):
            suitable_agents.append({
                "agent_type": agent_type,
                "capability_level": agent_capability,
                "availability": agent_info.is_available()
            })
    
    # Sort by capability level and availability
    suitable_agents.sort(key=lambda x: (x["capability_level"], x["availability"]), reverse=True)
    
    return suitable_agents[0]["agent_type"] if suitable_agents else None
```

## First Delegation Procedures

### Initial Agent Setup

When setting up agent delegation for the first time, follow these comprehensive procedures:

#### 1. Framework Initialization

```bash
# Verify framework initialization
python ~/.claude/commands/cmpm-bridge.py cmcp-init --verify

# Expected output:
# ✅ Agent hierarchy structure verified
# ✅ System agents available
# ✅ User agent directory created
# ✅ Project agent structure ready
```

#### 2. Agent Discovery and Validation

```python
from claude_pm.services.multi_agent_orchestrator import MultiAgentOrchestrator

async def initialize_first_delegation():
    """Initialize framework for first delegation session."""
    
    orchestrator = MultiAgentOrchestrator()
    
    # 1. Discover available agents
    available_agents = await orchestrator.discover_agents()
    print(f"Discovered {len(available_agents)} agents")
    
    # 2. Validate agent hierarchy
    hierarchy_status = await orchestrator.validate_agent_hierarchy()
    
    if not hierarchy_status.valid:
        print("Agent hierarchy issues detected:")
        for issue in hierarchy_status.issues:
            print(f"  - {issue}")
        return False
    
    # 3. Test basic delegation
    test_result = await orchestrator.test_delegation("research", {
        "task": "Framework initialization test",
        "context": "First delegation validation"
    })
    
    if test_result.success:
        print("✅ First delegation test successful")
        return True
    else:
        print(f"❌ Delegation test failed: {test_result.error}")
        return False
```

#### 3. Agent Communication Protocol Setup

```python
class FirstDelegationProtocol:
    """Protocol for establishing first delegation session."""
    
    def __init__(self):
        self.communication_channels = {}
        self.delegation_history = []
    
    async def establish_communication(self, agent_type: str) -> bool:
        """Establish communication channel with agent."""
        
        try:
            # 1. Initialize agent
            agent = await self.load_agent(agent_type)
            
            # 2. Perform handshake
            handshake_result = await agent.handshake({
                "session_id": self.session_id,
                "timestamp": datetime.now().isoformat(),
                "protocol_version": "4.5.1"
            })
            
            if handshake_result.success:
                self.communication_channels[agent_type] = {
                    "agent": agent,
                    "status": "active",
                    "established_at": datetime.now().isoformat()
                }
                return True
            else:
                print(f"Handshake failed with {agent_type}: {handshake_result.error}")
                return False
                
        except Exception as e:
            print(f"Failed to establish communication with {agent_type}: {e}")
            return False
    
    async def first_delegation_workflow(self):
        """Execute complete first delegation workflow."""
        
        # 1. Establish communication with core agents
        core_agents = ["research", "engineer", "qa", "ops"]
        
        for agent_type in core_agents:
            success = await self.establish_communication(agent_type)
            if not success:
                print(f"⚠️  Could not establish communication with {agent_type}")
        
        # 2. Perform first delegation
        first_task = {
            "task": "Framework status assessment",
            "agent_type": "research",
            "priority": "high",
            "context": {
                "purpose": "First delegation validation",
                "expected_deliverables": ["status_report", "recommendations"]
            }
        }
        
        result = await self.delegate_task(first_task)
        
        # 3. Record delegation history
        self.delegation_history.append({
            "timestamp": datetime.now().isoformat(),
            "task": first_task,
            "result": result,
            "status": "completed" if result.success else "failed"
        })
        
        return result
```

## Best Practices

### Agent Design Principles

1. **Single Responsibility**: Each agent should have a clear, focused responsibility
2. **Loose Coupling**: Agents should be independent and communicate through well-defined interfaces
3. **High Cohesion**: Related functionality should be grouped within the same agent
4. **Scalability**: Agents should be designed to handle increasing workloads
5. **Fault Tolerance**: Agents should gracefully handle errors and continue operation

### Delegation Best Practices

1. **Clear Task Definition**: Provide detailed task descriptions with context
2. **Appropriate Agent Selection**: Choose agents based on capabilities and availability
3. **Context Propagation**: Pass relevant context between agents
4. **Error Handling**: Implement comprehensive error handling and recovery
5. **Monitoring**: Monitor agent performance and health continuously

### Communication Patterns

1. **Asynchronous Communication**: Use async patterns for non-blocking operations
2. **Message Passing**: Implement structured message passing between agents
3. **Event-Driven Architecture**: Use events for loose coupling between agents
4. **State Management**: Maintain consistent state across agent interactions
5. **Logging**: Implement comprehensive logging for debugging and monitoring

## Troubleshooting

### Common Delegation Issues

#### Agent Not Found
**Symptoms**: Agent type not recognized or not available
**Solutions**:
1. Verify agent exists in hierarchy
2. Check agent registration
3. Validate agent loading path
4. Review agent discovery logs

#### Delegation Timeout
**Symptoms**: Task delegation times out without completion
**Solutions**:
1. Check agent responsiveness
2. Increase timeout values
3. Review task complexity
4. Monitor system resources

#### Communication Failures
**Symptoms**: Agents fail to communicate or respond
**Solutions**:
1. Verify communication channels
2. Check network connectivity
3. Review protocol compatibility
4. Restart agent processes

#### Context Propagation Issues
**Symptoms**: Context not properly passed between agents
**Solutions**:
1. Validate context structure
2. Check serialization/deserialization
3. Review context filtering
4. Monitor context size limits

### Debug Information

Enable comprehensive debugging for agent delegation:

```python
import logging

# Enable debug logging for agent systems
logging.getLogger("claude_pm.agents").setLevel(logging.DEBUG)
logging.getLogger("claude_pm.orchestrator").setLevel(logging.DEBUG)
logging.getLogger("claude_pm.delegation").setLevel(logging.DEBUG)

# Agent-specific debugging
orchestrator = MultiAgentOrchestrator(debug=True)

# Print agent hierarchy status
hierarchy_info = await orchestrator.get_hierarchy_info()
for tier, agents in hierarchy_info.items():
    print(f"Tier {tier}: {len(agents)} agents")
    for agent in agents:
        print(f"  - {agent.agent_type}: {agent.status}")

# Test agent availability
for agent_type in ["engineer", "qa", "ops", "research"]:
    available = await orchestrator.is_agent_available(agent_type)
    print(f"{agent_type}: {'✅' if available else '❌'}")
```

## Summary

This comprehensive agents and delegation guide provides:

### Core Agent Management
- **Three-Tier Hierarchy**: Project → User → System agent loading with automatic fallback
- **11-Agent Ecosystem**: Core production and specialized support agents
- **Professional Display Names**: Systematic naming convention for clear identification
- **User-Defined Agents**: Strategy for creating and integrating custom agents

### Delegation Framework
- **Pattern Recognition**: Automatic task delegation based on request patterns
- **Coordination Matrix**: Clear authority and escalation paths for all agent types
- **Multi-Agent Orchestration**: Sequential, parallel, and hierarchical delegation patterns
- **Capability-Based Selection**: Intelligent agent selection based on task requirements

### Orchestration Features
- **First Delegation Procedures**: Complete setup and validation workflow
- **Communication Protocols**: Robust agent communication and handshake procedures
- **Error Handling**: Comprehensive error handling and recovery mechanisms
- **Performance Monitoring**: Continuous monitoring of agent health and performance

### Best Practices
- **Design Principles**: Single responsibility, loose coupling, and fault tolerance
- **Communication Patterns**: Asynchronous, event-driven, and state-consistent operations
- **Troubleshooting**: Common issues and debugging procedures for agent systems

The Claude PM Framework agent and delegation system ensures efficient, scalable, and reliable multi-agent coordination through systematic delegation strategies, comprehensive monitoring, and robust error handling.

---

**Framework Version**: 4.5.1  
**Last Updated**: 2025-07-11  
**Agents Guide Version**: 2.0.0  
**Authority Level**: Complete Agent Management