# Design Patterns Catalog

Comprehensive catalog of design patterns for the Kaizen Framework, including current implementations, planned enhancements, and best practices.

## Pattern Categories

### 1. **Framework Patterns** - Core framework usage
### 2. **Agent Patterns** - AI agent design and composition
### 3. **Integration Patterns** - Kailash SDK and framework integration
### 4. **Enterprise Patterns** - Production deployment and governance
### 5. **Performance Patterns** - Optimization and scaling

---

## Framework Patterns

### Pattern F1: Basic Framework Initialization

**Status**: ✅ Available
**Use Case**: Standard framework setup with configuration

```python
# Basic initialization
from kaizen import Kaizen

kaizen = Kaizen()

# With configuration
kaizen = Kaizen(config={
    'default_model': 'gpt-4',
    'temperature': 0.7,
    'performance_tracking': True,
    'cache_enabled': True
})
```

**When to Use**:
- Standard application setup
- Development and testing environments
- Simple configuration requirements

**Best Practices**:
- Use configuration files for complex setups
- Enable performance tracking in development
- Set sensible defaults for production

### Pattern F2: Configuration Hierarchy

**Status**: ✅ Available
**Use Case**: Layered configuration management

```python
# System-level configuration
system_config = {
    'logging_level': 'INFO',
    'performance_tracking': True,
    'cache_enabled': True
}

# Framework configuration
kaizen = Kaizen(config={
    'default_model': 'gpt-4',
    'temperature': 0.7,
    **system_config
})

# Agent-specific overrides
agent = kaizen.create_agent("specialized", {
    'model': 'gpt-3.5-turbo',  # Override framework default
    'temperature': 0.9,        # Override framework default
    'max_tokens': 2000         # Agent-specific setting
})
```

**Configuration Precedence**:
1. Runtime parameters (highest priority)
2. Agent-specific configuration
3. Framework configuration
4. System defaults (lowest priority)

**Best Practices**:
- Use environment variables for sensitive configuration
- Document configuration options clearly
- Validate configuration at initialization

### Pattern F3: Error-Resistant Framework Usage

**Status**: ✅ Available
**Use Case**: Production-ready error handling

```python
from kaizen import Kaizen
from kaizen.core.exceptions import KaizenError, ConfigurationError
import logging

def create_robust_framework():
    """Create framework with comprehensive error handling"""
    try:
        # Validate configuration before initialization
        config = {
            'default_model': 'gpt-4',
            'temperature': 0.7,
            'timeout': 30
        }

        # Initialize with validation
        kaizen = Kaizen(config=config)

        # Verify framework health
        health_check = kaizen.get_health_status()
        if not health_check.is_healthy:
            raise KaizenError(f"Framework health check failed: {health_check.issues}")

        logging.info("✅ Kaizen framework initialized successfully")
        return kaizen

    except ConfigurationError as e:
        logging.error(f"❌ Configuration error: {e}")
        # Fallback to minimal configuration
        return Kaizen()

    except KaizenError as e:
        logging.error(f"❌ Framework error: {e}")
        raise

    except Exception as e:
        logging.error(f"❌ Unexpected error: {e}")
        raise KaizenError(f"Framework initialization failed: {e}")

# Usage
kaizen = create_robust_framework()
```

**Error Handling Strategy**:
- Validate configuration early
- Provide meaningful error messages
- Implement graceful degradation
- Log errors for debugging

---

## Agent Patterns

### Pattern A1: Basic Agent Creation

**Status**: ✅ Available
**Use Case**: Standard AI agent for text processing

```python
from kaizen import Kaizen
from kailash.runtime.local import LocalRuntime

# Initialize framework
kaizen = Kaizen()

# Create agent with specific configuration
agent = kaizen.create_agent("text_processor", {
    "model": "gpt-4",
    "temperature": 0.7,
    "max_tokens": 1000,
    "system_prompt": "You are a helpful text processing assistant"
})

# Execute using Core SDK runtime
runtime = LocalRuntime()
results, run_id = runtime.execute(agent.workflow.build())

print(f"Agent executed: {run_id}")
print(f"Results: {results}")
```

**When to Use**:
- Simple text processing tasks
- Single-agent applications
- Development and prototyping

### Pattern A2: Specialized Agent Configuration

**Status**: ✅ Available
**Use Case**: Domain-specific agents with tailored behavior

```python
# Research Assistant Agent
research_agent = kaizen.create_agent("research_assistant", {
    "model": "gpt-4",
    "temperature": 0.3,  # Lower temperature for factual accuracy
    "max_tokens": 2000,
    "system_prompt": """You are a research assistant that provides
    comprehensive, well-sourced information on any topic. Always cite
    sources when possible and indicate confidence levels."""
})

# Creative Writing Agent
creative_agent = kaizen.create_agent("creative_writer", {
    "model": "gpt-4",
    "temperature": 0.9,  # Higher temperature for creativity
    "max_tokens": 3000,
    "system_prompt": """You are a creative writing assistant that helps
    with storytelling, poetry, and creative content. Focus on originality,
    style, and engaging narratives."""
})

# Code Analysis Agent
code_agent = kaizen.create_agent("code_analyzer", {
    "model": "gpt-4",
    "temperature": 0.1,  # Very low temperature for precise analysis
    "max_tokens": 2000,
    "system_prompt": """You are a code analysis expert. Provide detailed
    code reviews, identify bugs, suggest improvements, and explain complex
    algorithms clearly."""
})
```

**Configuration Guidelines**:
- **Temperature**: 0.1-0.3 for factual/analytical, 0.7-0.9 for creative
- **Max Tokens**: Adjust based on expected response length
- **System Prompt**: Clear role definition and behavior guidelines

### Pattern A3: Agent with Memory Context (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Conversational agents that maintain context

```python
# Future pattern - not currently implemented
agent = kaizen.create_agent("conversational_assistant", {
    "model": "gpt-4",
    "temperature": 0.7,
    "memory_enabled": True,
    "memory_type": "vector",
    "context_window": 10000,
    "memory_decay": "exponential"  # Older memories fade over time
})

# Usage will maintain conversation context
response1 = agent.execute("My name is John and I work in finance")
response2 = agent.execute("What do you remember about me?")
# Agent will remember John works in finance
```

### Pattern A4: Multi-Agent Coordination (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Multiple agents working together on complex tasks

```python
# Future multi-agent pattern - not currently implemented
kaizen = Kaizen(config={'multi_agent_enabled': True})

# Create specialized agents
researcher = kaizen.create_specialized_agent(
    name="researcher",
    role="information_gathering",
    config={
        "model": "gpt-4",
        "tools": ["search", "analyze"],
        "expertise": ["technology", "business"]
    }
)

analyst = kaizen.create_specialized_agent(
    name="analyst",
    role="data_analysis",
    config={
        "model": "gpt-4",
        "tools": ["calculate", "visualize"],
        "expertise": ["statistics", "forecasting"]
    }
)

writer = kaizen.create_specialized_agent(
    name="writer",
    role="content_creation",
    config={
        "model": "gpt-4",
        "style": "professional",
        "format": "structured_report"
    }
)

# Coordination patterns
team_workflow = kaizen.create_team_workflow([researcher, analyst, writer])
report = team_workflow.execute("Analyze the AI market trends for 2024")
```

---

## Integration Patterns

### Pattern I1: Core SDK Compatibility

**Status**: ✅ Available
**Use Case**: Seamless integration with existing Core SDK workflows

```python
from kaizen import Kaizen
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime

# Traditional Core SDK approach
traditional_workflow = WorkflowBuilder()
traditional_workflow.add_node("LLMAgentNode", "agent", {
    "model": "gpt-4",
    "prompt_template": "Process: {input}",
    "temperature": 0.7
})

# Kaizen enhancement approach
kaizen = Kaizen()
agent = kaizen.create_agent("processor", {
    "model": "gpt-4",
    "temperature": 0.7
})

# Both use the same runtime and execution pattern
runtime = LocalRuntime()

# Execute traditional workflow
results1, run_id1 = runtime.execute(traditional_workflow.build())

# Execute Kaizen agent workflow
results2, run_id2 = runtime.execute(agent.workflow.build())

print("✅ Both approaches work with the same runtime!")
```

**Key Benefits**:
- **Zero Migration Risk**: Existing workflows continue working unchanged
- **Gradual Enhancement**: Can adopt Kaizen features incrementally
- **Runtime Compatibility**: Uses same execution infrastructure

### Pattern I2: Workflow Composition

**Status**: ✅ Available
**Use Case**: Combining Kaizen agents with Core SDK nodes

```python
from kailash.workflow.builder import WorkflowBuilder
from kaizen import Kaizen

# Create Kaizen agent
kaizen = Kaizen()
agent = kaizen.create_agent("analyzer", {
    "model": "gpt-4",
    "temperature": 0.3
})

# Build composite workflow
workflow = WorkflowBuilder()

# Add traditional Core SDK nodes
workflow.add_node("DataLoaderNode", "loader", {
    "source": "database",
    "query": "SELECT * FROM reports"
})

workflow.add_node("DataTransformerNode", "transformer", {
    "format": "json"
})

# Convert Kaizen agent to workflow node (future feature)
# agent_node = agent.to_workflow_node()
# workflow.add_node_instance(agent_node)
# For now, manually integrate agent workflow
agent_workflow = agent.workflow
workflow.merge_workflow(agent_workflow, "analyzer")

workflow.add_node("OutputFormatterNode", "formatter", {
    "format": "report"
})

# Connect nodes
workflow.add_connection("loader", "transformer")
workflow.add_connection("transformer", "analyzer")
workflow.add_connection("analyzer", "formatter")

# Execute composite workflow
runtime = LocalRuntime()
results, run_id = runtime.execute(workflow.build())
```

### Pattern I3: DataFlow Integration (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Database-aware AI agents

```python
# Future DataFlow integration pattern
from kaizen import Kaizen
from dataflow import db

# Define database model
@db.model
class CustomerQuery:
    question: str
    customer_id: str
    priority: str = "normal"

@db.model
class CustomerResponse:
    answer: str
    confidence: float
    resources_used: List[str]

# Create database-aware agent
@kaizen.signature("query -> response")
@dataflow.database_context("customer_db")
def customer_service_agent(query: CustomerQuery) -> CustomerResponse:
    """Customer service agent with automatic database context"""
    # Agent automatically understands customer history, preferences, etc.
    pass

# Usage
query = CustomerQuery(
    question="What's my order status?",
    customer_id="CUST_12345"
)
response = customer_service_agent(query)
```

### Pattern I4: Nexus Multi-Channel Deployment (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Deploy agents across multiple access channels

```python
# Future Nexus integration pattern
from kaizen import Kaizen
import nexus

kaizen = Kaizen()
agent = kaizen.create_agent("customer_service", {
    "model": "gpt-4",
    "temperature": 0.7
})

# Deploy as multi-channel service
service = agent.deploy_as_nexus(
    name="customer-service-v1",
    channels={
        "api": {
            "port": 8080,
            "auth": "jwt",
            "rate_limit": "100/min"
        },
        "cli": {
            "command": "customer-service",
            "auth": "system"
        },
        "mcp": {
            "server_name": "customer-service-mcp",
            "capabilities": ["answer_questions", "lookup_orders"]
        }
    },
    monitoring=True,
    scaling="auto"
)

# Unified session management across channels
session = service.create_session(user_id="user123", channel="api")
response = session.execute("What's my order status?")
```

---

## Enterprise Patterns

### Pattern E1: Security and Access Control (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Enterprise security and governance

```python
# Future enterprise security pattern
kaizen = Kaizen(config={
    'security_profile': 'enterprise',
    'auth_provider': 'active_directory',
    'encryption': 'end_to_end',
    'audit_enabled': True
})

# Agent with security controls
secure_agent = kaizen.create_agent("financial_analyzer", {
    "model": "gpt-4",
    "required_permissions": [
        "financial_data_read",
        "sensitive_analysis"
    ],
    "security_clearance": "confidential",
    "data_retention": "90_days",
    "audit_level": "comprehensive"
})

# Access control validation
user_context = {
    "user_id": "analyst123",
    "permissions": ["financial_data_read", "sensitive_analysis"],
    "clearance": "confidential"
}

# Secure execution with audit trail
audit_context = kaizen.create_audit_context(user_context)
with audit_context:
    results = secure_agent.execute("Analyze Q3 revenue trends")

# Audit trail automatically captured
audit_log = audit_context.get_audit_log()
```

### Pattern E2: Monitoring and Observability (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Production monitoring and debugging

```python
# Future monitoring pattern
kaizen = Kaizen(config={
    'transparency_enabled': True,
    'monitoring_level': 'detailed',
    'metrics_export': ['prometheus', 'datadog'],
    'tracing_enabled': True
})

# Agent with monitoring
monitored_agent = kaizen.create_agent("production_processor", {
    "model": "gpt-4",
    "monitoring": {
        "performance_tracking": True,
        "error_tracking": True,
        "cost_tracking": True,
        "quality_metrics": True
    }
})

# Monitoring interface
transparency = kaizen.get_transparency_interface()
monitor = transparency.create_workflow_monitor()

# Execute with monitoring
with monitor.trace("customer_query_processing"):
    results = monitored_agent.execute(query)

# Access monitoring data
metrics = monitor.get_metrics()
performance_data = monitor.get_performance_data()
cost_analysis = monitor.get_cost_analysis()

print(f"Execution time: {metrics.execution_time}ms")
print(f"Token usage: {metrics.tokens_used}")
print(f"Cost: ${metrics.estimated_cost:.4f}")
```

### Pattern E3: Compliance and Governance (Planned)

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Regulatory compliance and governance

```python
# Future compliance pattern
compliance_config = {
    'regulations': ['gdpr', 'hipaa', 'soc2'],
    'data_classification': 'restricted',
    'retention_policy': '7_years',
    'anonymization': 'automatic',
    'consent_management': True
}

kaizen = Kaizen(config={
    'compliance_profile': 'healthcare',
    **compliance_config
})

# Compliance-aware agent
healthcare_agent = kaizen.create_agent("medical_assistant", {
    "model": "gpt-4",
    "compliance": {
        "hipaa_compliant": True,
        "phi_handling": "encrypted",
        "audit_trail": "detailed",
        "consent_required": True
    }
})

# Compliance validation
compliance_validator = kaizen.get_compliance_validator()
compliance_status = compliance_validator.validate_agent(healthcare_agent)

if compliance_status.is_compliant:
    # Safe to execute
    results = healthcare_agent.execute(patient_query)

    # Compliance reporting
    compliance_report = compliance_validator.generate_report()
    compliance_report.export("compliance_report.pdf")
```

---

## Performance Patterns

### Pattern P1: Performance Optimization

**Status**: ✅ Available (basic), 🟡 Advanced features planned
**Use Case**: Optimizing framework and agent performance

```python
# Current performance optimization
kaizen = Kaizen(config={
    'lazy_loading': True,          # Load components only when needed
    'cache_enabled': True,         # Enable intelligent caching
    'performance_tracking': True,  # Track performance metrics
    'import_optimization': True    # Optimize import performance
})

# Agent performance configuration
optimized_agent = kaizen.create_agent("high_perf_processor", {
    "model": "gpt-3.5-turbo",  # Faster model for speed
    "temperature": 0.3,
    "max_tokens": 1000,
    "stream": True,            # Enable streaming responses
    "cache_responses": True    # Cache similar responses
})

# Performance measurement
import time

start_time = time.time()
results = optimized_agent.execute(query)
execution_time = (time.time() - start_time) * 1000

print(f"Execution time: {execution_time:.0f}ms")

# Performance optimization recommendations (future)
# optimizer = kaizen.get_performance_optimizer()
# recommendations = optimizer.analyze_agent(optimized_agent)
# optimized_config = optimizer.optimize_config(agent_config, target_latency=100)
```

### Pattern P2: Caching Strategies

**Status**: ✅ Basic caching available
**Use Case**: Reducing redundant processing and costs

```python
# Framework-level caching
kaizen = Kaizen(config={
    'cache_enabled': True,
    'cache_backend': 'memory',  # or 'redis', 'database'
    'cache_ttl': 3600,         # 1 hour TTL
    'cache_size_limit': '1GB'
})

# Agent-specific caching
cached_agent = kaizen.create_agent("cached_processor", {
    "model": "gpt-4",
    "caching": {
        "enable_response_cache": True,
        "cache_key_strategy": "content_hash",
        "cache_ttl": 1800,  # 30 minutes
        "cache_similar_queries": True,
        "similarity_threshold": 0.9
    }
})

# Cache management
cache_manager = kaizen.get_cache_manager()

# Check cache status
cache_stats = cache_manager.get_stats()
print(f"Cache hit ratio: {cache_stats.hit_ratio:.2%}")
print(f"Cache size: {cache_stats.size_mb:.1f}MB")

# Manual cache operations
cache_manager.clear_cache()  # Clear all caches
cache_manager.clear_agent_cache(cached_agent)  # Clear agent-specific cache
```

### Pattern P3: Batch Processing

**Status**: 🟡 Architecture designed, not implemented
**Use Case**: Processing multiple requests efficiently

```python
# Future batch processing pattern
batch_agent = kaizen.create_agent("batch_processor", {
    "model": "gpt-4",
    "batch_config": {
        "max_batch_size": 10,
        "batch_timeout": 5000,  # 5 seconds
        "auto_batching": True
    }
})

# Batch execution
queries = [
    "Summarize this document",
    "Translate to Spanish",
    "Extract key points",
    # ... more queries
]

# Automatic batching for efficiency
results = batch_agent.execute_batch(queries)

# Or manual batch control
with batch_agent.batch_context() as batch:
    batch.add_query("Query 1")
    batch.add_query("Query 2")
    batch.add_query("Query 3")
    results = batch.execute()
```

---

## Testing Patterns

### Pattern T1: Agent Testing

**Status**: ✅ Available
**Use Case**: Comprehensive agent testing strategies

```python
import pytest
from kaizen import Kaizen
from kailash.runtime.local import LocalRuntime

class TestKaizenAgent:
    def setup_method(self):
        """Setup for each test"""
        self.kaizen = Kaizen(config={
            'test_mode': True,
            'cache_enabled': False  # Disable caching for tests
        })
        self.runtime = LocalRuntime()

    def test_agent_creation(self):
        """Test agent creation and configuration"""
        agent = self.kaizen.create_agent("test_agent", {
            "model": "gpt-3.5-turbo",
            "temperature": 0.5
        })

        assert agent is not None
        assert agent.name == "test_agent"
        assert agent.config["model"] == "gpt-3.5-turbo"
        assert agent.config["temperature"] == 0.5

    def test_agent_workflow_execution(self):
        """Test agent workflow execution"""
        agent = self.kaizen.create_agent("test_executor", {
            "model": "gpt-3.5-turbo"
        })

        # Execute workflow
        results, run_id = self.runtime.execute(agent.workflow.build())

        assert run_id is not None
        assert results is not None

    def test_agent_error_handling(self):
        """Test agent error handling"""
        with pytest.raises(ValueError):
            # Invalid configuration should raise error
            self.kaizen.create_agent("invalid", {
                "model": "invalid_model_name"
            })

    def test_agent_performance(self):
        """Test agent performance baseline"""
        import time

        agent = self.kaizen.create_agent("perf_test", {
            "model": "gpt-3.5-turbo"
        })

        start_time = time.time()
        results, run_id = self.runtime.execute(agent.workflow.build())
        execution_time = (time.time() - start_time) * 1000

        # Performance assertions
        assert execution_time < 5000  # Less than 5 seconds
        assert results is not None
```

### Pattern T2: Integration Testing

**Status**: ✅ Available
**Use Case**: Testing framework integration with Core SDK

```python
import pytest
from kaizen import Kaizen
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime

class TestKaizenIntegration:
    def test_core_sdk_compatibility(self):
        """Test compatibility with Core SDK patterns"""
        # Traditional Core SDK workflow
        traditional_workflow = WorkflowBuilder()
        traditional_workflow.add_node("LLMAgentNode", "agent", {
            "model": "gpt-3.5-turbo"
        })

        # Kaizen agent workflow
        kaizen = Kaizen()
        agent = kaizen.create_agent("compat_test", {
            "model": "gpt-3.5-turbo"
        })

        # Both should work with same runtime
        runtime = LocalRuntime()

        results1, run_id1 = runtime.execute(traditional_workflow.build())
        results2, run_id2 = runtime.execute(agent.workflow.build())

        assert run_id1 is not None
        assert run_id2 is not None
        assert results1 is not None
        assert results2 is not None

    def test_workflow_composition(self):
        """Test composing Kaizen agents with Core SDK nodes"""
        kaizen = Kaizen()
        agent = kaizen.create_agent("composer_test", {
            "model": "gpt-3.5-turbo"
        })

        # Create composite workflow
        workflow = WorkflowBuilder()
        workflow.add_node("DataLoaderNode", "loader", {})

        # Merge agent workflow (simplified)
        agent_workflow = agent.workflow
        # In practice: workflow.merge_workflow(agent_workflow)

        runtime = LocalRuntime()
        results, run_id = runtime.execute(workflow.build())

        assert run_id is not None
```

## Best Practices Summary

### 1. **Configuration Management**
- Use hierarchical configuration with clear precedence
- Validate configuration at initialization
- Separate development and production configurations
- Use environment variables for sensitive data

### 2. **Error Handling**
- Implement comprehensive error handling at all levels
- Provide meaningful error messages and recovery suggestions
- Use structured logging for debugging and monitoring
- Implement graceful degradation where possible

### 3. **Performance Optimization**
- Enable caching for repeated operations
- Use appropriate model selection based on requirements
- Implement batch processing for multiple requests
- Monitor and measure performance regularly

### 4. **Testing Strategy**
- Test agent creation and configuration
- Validate workflow execution and results
- Test error conditions and edge cases
- Measure performance baselines and regressions

### 5. **Security and Compliance**
- Validate inputs and sanitize outputs
- Implement proper access controls
- Maintain comprehensive audit trails
- Follow enterprise security best practices

## Complete Agent Design Patterns (34 Patterns)

### Single-Agent Patterns (8 patterns)

#### SA1: Basic Question-Answering Agent
**Status**: ✅ Available
**Complexity**: Simple
**Use Case**: Straightforward Q&A with structured responses

```python
class BasicQASignature(dspy.Signature):
    """Basic question-answering with confidence scoring."""
    question: str = dspy.InputField(desc="User question")
    context: str = dspy.InputField(desc="Optional context", default="")

    answer: str = dspy.OutputField(desc="Direct answer to question")
    confidence: float = dspy.OutputField(desc="Confidence level (0.0-1.0)")
    reasoning: str = dspy.OutputField(desc="Brief reasoning behind answer")

basic_qa_agent = kaizen.create_agent("basic_qa", {
    "model": "gpt-4",
    "signature": BasicQASignature,
    "temperature": 0.3
})
```

#### SA2: Chain-of-Thought Reasoning Agent
**Status**: ✅ Available
**Complexity**: Medium
**Use Case**: Complex reasoning requiring step-by-step breakdown

```python
class ChainOfThoughtSignature(dspy.Signature):
    """Chain-of-thought reasoning for complex problems."""
    problem: str = dspy.InputField(desc="Complex problem to solve")
    problem_type: str = dspy.InputField(desc="Type: math, logic, analysis")

    reasoning_steps: List[str] = dspy.OutputField(desc="Step-by-step reasoning")
    solution: str = dspy.OutputField(desc="Final solution")
    verification: str = dspy.OutputField(desc="Solution verification")
    confidence: float = dspy.OutputField(desc="Solution confidence")

cot_agent = kaizen.create_agent("chain_of_thought", {
    "model": "gpt-4",
    "signature": ChainOfThoughtSignature,
    "temperature": 0.2
})
```

#### SA3: ReAct (Reasoning + Acting) Agent
**Status**: ✅ Available
**Complexity**: High
**Use Case**: Dynamic action planning with external tool integration

```python
class ReActSignature(dspy.Signature):
    """ReAct pattern: Reasoning and Acting iteratively."""
    goal: str = dspy.InputField(desc="Objective to accomplish")
    available_tools: List[str] = dspy.InputField(desc="Available tools/actions")
    context: str = dspy.InputField(desc="Current context/state")

    thought: str = dspy.OutputField(desc="Current reasoning/thinking")
    action: str = dspy.OutputField(desc="Action to take")
    action_input: Dict = dspy.OutputField(desc="Input for the action")
    observation_expected: str = dspy.OutputField(desc="Expected observation")

react_agent = kaizen.create_agent("react_agent", {
    "model": "gpt-4",
    "signature": ReActSignature,
    "temperature": 0.4,
    "tools_integration": True
})
```

#### SA4: Self-Reflection Agent
**Status**: ✅ Available
**Complexity**: High
**Use Case**: Self-improving agents that critique their own outputs

```python
class SelfReflectionSignature(dspy.Signature):
    """Self-reflection and improvement pattern."""
    task: str = dspy.InputField(desc="Task to perform")
    initial_response: str = dspy.InputField(desc="Initial response to critique")
    quality_criteria: List[str] = dspy.InputField(desc="Quality criteria to assess")

    self_critique: str = dspy.OutputField(desc="Critique of initial response")
    improvement_areas: List[str] = dspy.OutputField(desc="Areas for improvement")
    revised_response: str = dspy.OutputField(desc="Improved response")
    quality_score: float = dspy.OutputField(desc="Self-assessed quality (0.0-1.0)")

reflection_agent = kaizen.create_agent("self_reflection", {
    "model": "gpt-4",
    "signature": SelfReflectionSignature,
    "temperature": 0.5,
    "self_improvement": True
})
```

#### SA5: Memory-Enhanced Agent
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Conversational agents with persistent memory

```python
# Future memory-enhanced agent pattern
class MemoryEnhancedSignature(dspy.Signature):
    """Agent with episodic and semantic memory."""
    current_input: str = dspy.InputField(desc="Current user input")
    conversation_history: List[Dict] = dspy.InputField(desc="Recent conversation")
    relevant_memories: List[str] = dspy.InputField(desc="Retrieved relevant memories")

    response: str = dspy.OutputField(desc="Response incorporating memory")
    memory_updates: List[Dict] = dspy.OutputField(desc="New memories to store")
    memory_importance: float = dspy.OutputField(desc="Importance of this interaction")
    personalization_level: float = dspy.OutputField(desc="Personalization applied")

memory_agent = kaizen.create_agent("memory_enhanced", {
    "model": "gpt-4",
    "signature": MemoryEnhancedSignature,
    "memory_system": {
        "type": "hybrid",  # episodic + semantic
        "capacity": 10000,
        "decay_function": "exponential"
    }
})
```

#### SA6: Multimodal Analysis Agent
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Processing and analyzing multiple data types

```python
# Future multimodal agent pattern
class MultimodalSignature(dspy.Signature):
    """Multimodal analysis across text, images, audio."""
    text_input: str = dspy.InputField(desc="Text content to analyze")
    image_input: str = dspy.InputField(desc="Image data (base64 or URL)")
    audio_input: str = dspy.InputField(desc="Audio data (base64 or URL)")
    analysis_type: str = dspy.InputField(desc="Type of analysis required")

    text_analysis: str = dspy.OutputField(desc="Text content analysis")
    image_analysis: str = dspy.OutputField(desc="Image content analysis")
    audio_analysis: str = dspy.OutputField(desc="Audio content analysis")
    cross_modal_insights: str = dspy.OutputField(desc="Insights across modalities")
    confidence_by_modality: Dict = dspy.OutputField(desc="Confidence per modality")

multimodal_agent = kaizen.create_agent("multimodal_analyzer", {
    "model": "gpt-4-vision",
    "signature": MultimodalSignature,
    "modalities": ["text", "image", "audio"],
    "cross_modal_reasoning": True
})
```

#### SA7: Code Generation Agent
**Status**: ✅ Available
**Complexity**: Medium
**Use Case**: Automated code generation with testing

```python
class CodeGenerationSignature(dspy.Signature):
    """Code generation with testing and documentation."""
    requirements: str = dspy.InputField(desc="Functional requirements")
    programming_language: str = dspy.InputField(desc="Target language")
    style_guide: str = dspy.InputField(desc="Code style requirements")

    generated_code: str = dspy.OutputField(desc="Generated code implementation")
    test_cases: List[str] = dspy.OutputField(desc="Unit test cases")
    documentation: str = dspy.OutputField(desc="Code documentation")
    complexity_analysis: str = dspy.OutputField(desc="Code complexity assessment")

code_gen_agent = kaizen.create_agent("code_generator", {
    "model": "gpt-4",
    "signature": CodeGenerationSignature,
    "temperature": 0.2,
    "code_validation": True
})
```

#### SA8: Research Assistant Agent
**Status**: ✅ Available
**Complexity**: High
**Use Case**: Comprehensive research with source validation

```python
class ResearchSignature(dspy.Signature):
    """Research assistant with source validation."""
    research_topic: str = dspy.InputField(desc="Topic to research")
    research_depth: str = dspy.InputField(desc="Depth: surface, detailed, comprehensive")
    source_requirements: List[str] = dspy.InputField(desc="Required source types")

    research_summary: str = dspy.OutputField(desc="Comprehensive research summary")
    key_findings: List[str] = dspy.OutputField(desc="Key research findings")
    source_citations: List[str] = dspy.OutputField(desc="Validated source citations")
    confidence_assessment: str = dspy.OutputField(desc="Confidence in findings")
    research_gaps: List[str] = dspy.OutputField(desc="Identified knowledge gaps")

research_agent = kaizen.create_agent("research_assistant", {
    "model": "gpt-4",
    "signature": ResearchSignature,
    "temperature": 0.3,
    "source_validation": True,
    "fact_checking": True
})
```

### Multi-Agent Coordination Patterns (6 patterns)

#### MA1: Producer-Consumer Pattern
**Status**: 🟡 Planned
**Complexity**: Medium
**Use Case**: Workflow with data processing pipeline

```python
# Future producer-consumer pattern
class ProducerSignature(dspy.Signature):
    """Producer agent for data generation."""
    data_requirements: str = dspy.InputField(desc="Data generation requirements")
    output_format: str = dspy.InputField(desc="Required output format")

    generated_data: str = dspy.OutputField(desc="Generated data")
    data_metadata: Dict = dspy.OutputField(desc="Data metadata")
    quality_score: float = dspy.OutputField(desc="Data quality assessment")

class ConsumerSignature(dspy.Signature):
    """Consumer agent for data processing."""
    input_data: str = dspy.InputField(desc="Data to process")
    processing_instructions: str = dspy.InputField(desc="Processing requirements")

    processed_data: str = dspy.OutputField(desc="Processed data")
    processing_summary: str = dspy.OutputField(desc="Processing summary")
    quality_validation: bool = dspy.OutputField(desc="Quality validation result")

# Multi-agent coordination (future)
producer_agent = kaizen.create_agent("data_producer", {
    "signature": ProducerSignature,
    "role": "producer"
})

consumer_agent = kaizen.create_agent("data_processor", {
    "signature": ConsumerSignature,
    "role": "consumer"
})

coordination = kaizen.create_coordination([producer_agent, consumer_agent], {
    "pattern": "producer_consumer",
    "queue_management": True,
    "error_handling": "retry_with_backoff"
})
```

#### MA2: Supervisor-Worker Pattern
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Task distribution and result aggregation

```python
# Future supervisor-worker pattern
class SupervisorSignature(dspy.Signature):
    """Supervisor agent for task coordination."""
    complex_task: str = dspy.InputField(desc="Complex task to coordinate")
    available_workers: List[str] = dspy.InputField(desc="Available worker agents")

    task_breakdown: List[Dict] = dspy.OutputField(desc="Task breakdown for workers")
    worker_assignments: Dict = dspy.OutputField(desc="Worker-task assignments")
    coordination_plan: str = dspy.OutputField(desc="Coordination plan")
    success_criteria: List[str] = dspy.OutputField(desc="Success criteria")

class WorkerSignature(dspy.Signature):
    """Worker agent for specialized tasks."""
    assigned_task: str = dspy.InputField(desc="Assigned subtask")
    task_context: str = dspy.InputField(desc="Task context and constraints")

    task_result: str = dspy.OutputField(desc="Task execution result")
    execution_status: str = dspy.OutputField(desc="Success/failure status")
    worker_feedback: str = dspy.OutputField(desc="Feedback for supervisor")

supervisor_worker_system = kaizen.create_multi_agent_system({
    "supervisor": SupervisorSignature,
    "workers": [WorkerSignature] * 3,  # 3 worker agents
    "coordination_pattern": "supervisor_worker",
    "result_aggregation": True
})
```

#### MA3: Debate and Decision Pattern
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Multi-perspective analysis and consensus building

```python
# Future debate and decision pattern
class DebaterSignature(dspy.Signature):
    """Debater agent with specific perspective."""
    topic: str = dspy.InputField(desc="Topic for debate")
    assigned_perspective: str = dspy.InputField(desc="Assigned viewpoint/perspective")
    opposing_arguments: List[str] = dspy.InputField(desc="Arguments from other debaters")

    position_statement: str = dspy.OutputField(desc="Position statement")
    supporting_arguments: List[str] = dspy.OutputField(desc="Supporting arguments")
    counterarguments: List[str] = dspy.OutputField(desc="Counter to opposing views")
    evidence_citations: List[str] = dspy.OutputField(desc="Supporting evidence")

class ModeratorSignature(dspy.Signature):
    """Moderator for debate synthesis."""
    debate_topic: str = dspy.InputField(desc="Debate topic")
    all_positions: List[str] = dspy.InputField(desc="All debater positions")
    all_arguments: List[str] = dspy.InputField(desc="All supporting arguments")

    synthesis: str = dspy.OutputField(desc="Synthesis of all perspectives")
    consensus_points: List[str] = dspy.OutputField(desc="Points of consensus")
    remaining_disputes: List[str] = dspy.OutputField(desc="Unresolved disputes")
    recommended_decision: str = dspy.OutputField(desc="Recommended course of action")

debate_system = kaizen.create_debate_system({
    "debaters": 3,
    "perspectives": ["optimistic", "pessimistic", "pragmatic"],
    "moderator": True,
    "rounds": 3,
    "consensus_threshold": 0.7
})
```

#### MA4: Domain Specialists Pattern
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Complex problems requiring multiple expertise areas

```python
# Future domain specialists pattern
class SpecialistSignature(dspy.Signature):
    """Domain specialist agent."""
    problem_statement: str = dspy.InputField(desc="Problem requiring expertise")
    domain_context: str = dspy.InputField(desc="Domain-specific context")
    other_specialist_input: List[str] = dspy.InputField(desc="Input from other specialists")

    domain_analysis: str = dspy.OutputField(desc="Analysis from domain perspective")
    recommendations: List[str] = dspy.OutputField(desc="Domain-specific recommendations")
    interdisciplinary_insights: str = dspy.OutputField(desc="Cross-domain insights")
    confidence_in_domain: float = dspy.OutputField(desc="Confidence in domain expertise")

class IntegratorSignature(dspy.Signature):
    """Integrator for specialist synthesis."""
    problem_statement: str = dspy.InputField(desc="Original problem statement")
    specialist_analyses: List[str] = dspy.InputField(desc="All specialist analyses")
    domain_recommendations: List[str] = dspy.InputField(desc="All recommendations")

    integrated_solution: str = dspy.OutputField(desc="Integrated solution")
    implementation_plan: List[str] = dspy.OutputField(desc="Implementation steps")
    risk_assessment: str = dspy.OutputField(desc="Risk assessment")
    success_metrics: List[str] = dspy.OutputField(desc="Success metrics")

specialist_system = kaizen.create_specialist_system({
    "domains": ["technical", "business", "legal", "ethical"],
    "integration_strategy": "weighted_consensus",
    "cross_domain_validation": True
})
```

#### MA5: Consensus Building Pattern
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Democratic decision-making with multiple agents

```python
# Future consensus building pattern
class ConsensusParticipantSignature(dspy.Signature):
    """Participant in consensus building."""
    proposal: str = dspy.InputField(desc="Proposal under consideration")
    participant_context: str = dspy.InputField(desc="Participant's context/interests")
    other_feedback: List[str] = dspy.InputField(desc="Feedback from other participants")

    initial_position: str = dspy.OutputField(desc="Initial position on proposal")
    concerns_raised: List[str] = dspy.OutputField(desc="Concerns about proposal")
    suggested_modifications: List[str] = dspy.OutputField(desc="Suggested changes")
    compromise_willingness: float = dspy.OutputField(desc="Willingness to compromise")

class ConsensusBuilderSignature(dspy.Signature):
    """Consensus building facilitator."""
    original_proposal: str = dspy.InputField(desc="Original proposal")
    participant_positions: List[str] = dspy.InputField(desc="All participant positions")
    concerns_list: List[str] = dspy.InputField(desc="All raised concerns")

    consensus_proposal: str = dspy.OutputField(desc="Modified proposal for consensus")
    addressed_concerns: List[str] = dspy.OutputField(desc="How concerns were addressed")
    consensus_level: float = dspy.OutputField(desc="Achieved consensus level")
    remaining_issues: List[str] = dspy.OutputField(desc="Unresolved issues")

consensus_system = kaizen.create_consensus_system({
    "participants": 5,
    "facilitation_style": "collaborative",
    "consensus_threshold": 0.8,
    "iteration_limit": 5
})
```

#### MA6: Human-AI Collaboration Pattern
**Status**: 🟡 Planned
**Complexity**: Medium
**Use Case**: Seamless human-AI collaborative workflows

```python
# Future human-AI collaboration pattern
class AICollaboratorSignature(dspy.Signature):
    """AI agent in human-AI collaboration."""
    collaborative_task: str = dspy.InputField(desc="Task requiring human-AI collaboration")
    human_input: str = dspy.InputField(desc="Input/feedback from human collaborator")
    collaboration_stage: str = dspy.InputField(desc="Current collaboration stage")

    ai_contribution: str = dspy.OutputField(desc="AI contribution to the task")
    human_guidance_needed: List[str] = dspy.OutputField(desc="Areas needing human input")
    collaboration_feedback: str = dspy.OutputField(desc="Feedback on collaboration")
    next_steps: List[str] = dspy.OutputField(desc="Suggested next steps")

class HumanInterfaceSignature(dspy.Signature):
    """Human interface management."""
    ai_output: str = dspy.InputField(desc="Output from AI collaborator")
    human_preferences: Dict = dspy.InputField(desc="Human collaboration preferences")

    formatted_output: str = dspy.OutputField(desc="Human-friendly formatted output")
    interaction_prompts: List[str] = dspy.OutputField(desc="Prompts for human input")
    collaboration_status: str = dspy.OutputField(desc="Current collaboration status")

collaboration_system = kaizen.create_collaboration_system({
    "ai_agents": ["analyst", "generator", "validator"],
    "human_interface": "adaptive",
    "interaction_mode": "real_time",
    "handoff_points": ["approval", "creative_input", "final_review"]
})
```

### Enterprise Workflow Patterns (6 patterns)

#### EW1: Document Processing Workflow
**Status**: ✅ Available
**Complexity**: Medium
**Use Case**: Automated document analysis and processing

```python
class DocumentProcessingSignature(dspy.Signature):
    """Enterprise document processing workflow."""
    document_content: str = dspy.InputField(desc="Document content to process")
    document_type: str = dspy.InputField(desc="Type: contract, report, invoice, etc.")
    processing_requirements: List[str] = dspy.InputField(desc="Processing requirements")

    extracted_data: Dict = dspy.OutputField(desc="Extracted structured data")
    document_summary: str = dspy.OutputField(desc="Document summary")
    compliance_check: Dict = dspy.OutputField(desc="Compliance validation results")
    processing_confidence: float = dspy.OutputField(desc="Processing confidence level")
    flagged_issues: List[str] = dspy.OutputField(desc="Issues requiring attention")

document_processor = kaizen.create_agent("document_processor", {
    "model": "gpt-4",
    "signature": DocumentProcessingSignature,
    "compliance_mode": True,
    "audit_trail": True
})
```

#### EW2: Customer Service Workflow
**Status**: ✅ Available
**Complexity**: High
**Use Case**: Multi-tier customer support with escalation

```python
class CustomerServiceSignature(dspy.Signature):
    """Customer service with escalation management."""
    customer_query: str = dspy.InputField(desc="Customer inquiry or issue")
    customer_history: List[str] = dspy.InputField(desc="Previous interaction history")
    urgency_level: str = dspy.InputField(desc="Low, medium, high, critical")

    response: str = dspy.OutputField(desc="Customer service response")
    resolution_status: str = dspy.OutputField(desc="Resolved, escalated, pending")
    escalation_reason: str = dspy.OutputField(desc="Reason for escalation if applicable")
    follow_up_needed: bool = dspy.OutputField(desc="Whether follow-up is required")
    satisfaction_prediction: float = dspy.OutputField(desc="Predicted customer satisfaction")

customer_service_agent = kaizen.create_agent("customer_service", {
    "model": "gpt-4",
    "signature": CustomerServiceSignature,
    "escalation_rules": True,
    "sentiment_analysis": True,
    "knowledge_base_integration": True
})
```

#### EW3: Compliance Monitoring Workflow
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Automated compliance checking and reporting

```python
# Future compliance monitoring pattern
class ComplianceMonitoringSignature(dspy.Signature):
    """Compliance monitoring and validation."""
    content_to_review: str = dspy.InputField(desc="Content requiring compliance review")
    applicable_regulations: List[str] = dspy.InputField(desc="Relevant regulations/standards")
    compliance_context: str = dspy.InputField(desc="Business context for compliance")

    compliance_status: str = dspy.OutputField(desc="Compliant, non-compliant, requires_review")
    violations_identified: List[str] = dspy.OutputField(desc="Specific violations found")
    remediation_steps: List[str] = dspy.OutputField(desc="Steps to achieve compliance")
    risk_assessment: str = dspy.OutputField(desc="Risk level assessment")
    regulatory_citations: List[str] = dspy.OutputField(desc="Relevant regulatory citations")

compliance_agent = kaizen.create_agent("compliance_monitor", {
    "model": "gpt-4",
    "signature": ComplianceMonitoringSignature,
    "regulatory_knowledge": ["gdpr", "hipaa", "soc2", "iso27001"],
    "audit_integration": True
})
```

#### EW4: Content Generation Workflow
**Status**: ✅ Available
**Complexity**: Medium
**Use Case**: Automated content creation with brand compliance

```python
class ContentGenerationSignature(dspy.Signature):
    """Enterprise content generation with brand compliance."""
    content_brief: str = dspy.InputField(desc="Content brief and requirements")
    target_audience: str = dspy.InputField(desc="Target audience description")
    brand_guidelines: str = dspy.InputField(desc="Brand voice and style guidelines")
    content_type: str = dspy.InputField(desc="Blog, email, social, marketing copy")

    generated_content: str = dspy.OutputField(desc="Generated content")
    brand_compliance_score: float = dspy.OutputField(desc="Brand compliance score")
    audience_alignment: float = dspy.OutputField(desc="Target audience alignment")
    seo_optimization: Dict = dspy.OutputField(desc="SEO optimization analysis")
    content_variations: List[str] = dspy.OutputField(desc="Alternative content versions")

content_generator = kaizen.create_agent("content_generator", {
    "model": "gpt-4",
    "signature": ContentGenerationSignature,
    "brand_consistency": True,
    "seo_optimization": True
})
```

#### EW5: Approval Workflow Pattern
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Multi-stage approval processes with routing

```python
# Future approval workflow pattern
class ApprovalRequestSignature(dspy.Signature):
    """Approval request processing."""
    request_content: str = dspy.InputField(desc="Content requiring approval")
    request_type: str = dspy.InputField(desc="Type of approval needed")
    requester_context: str = dspy.InputField(desc="Requester information and context")

    approval_routing: List[str] = dspy.OutputField(desc="Required approval chain")
    risk_assessment: str = dspy.OutputField(desc="Risk assessment of request")
    approval_recommendation: str = dspy.OutputField(desc="AI recommendation")
    required_documentation: List[str] = dspy.OutputField(desc="Required supporting docs")
    estimated_timeline: str = dspy.OutputField(desc="Estimated approval timeline")

class ApprovalReviewSignature(dspy.Signature):
    """Individual approval review."""
    approval_request: str = dspy.InputField(desc="Request under review")
    reviewer_role: str = dspy.InputField(desc="Reviewer's role and authority")
    previous_approvals: List[str] = dspy.InputField(desc="Previous approvals in chain")

    approval_decision: str = dspy.OutputField(desc="Approve, reject, request_changes")
    decision_rationale: str = dspy.OutputField(desc="Reasoning for decision")
    conditions_required: List[str] = dspy.OutputField(desc="Conditions if conditional approval")
    next_steps: str = dspy.OutputField(desc="Next steps in process")

approval_system = kaizen.create_approval_workflow({
    "approval_chains": {
        "financial": ["manager", "director", "cfo"],
        "legal": ["legal_counsel", "general_counsel"],
        "technical": ["tech_lead", "architect", "cto"]
    },
    "parallel_approvals": True,
    "escalation_handling": True
})
```

#### EW6: Data Reporting Workflow
**Status**: ✅ Available
**Complexity**: Medium
**Use Case**: Automated report generation and distribution

```python
class DataReportingSignature(dspy.Signature):
    """Automated data reporting and analysis."""
    data_source: str = dspy.InputField(desc="Data source specification")
    report_requirements: str = dspy.InputField(desc="Report requirements and format")
    reporting_period: str = dspy.InputField(desc="Time period for report")
    stakeholder_audience: str = dspy.InputField(desc="Target audience for report")

    report_content: str = dspy.OutputField(desc="Generated report content")
    key_insights: List[str] = dspy.OutputField(desc="Key insights and findings")
    visualizations: List[Dict] = dspy.OutputField(desc="Recommended visualizations")
    action_items: List[str] = dspy.OutputField(desc="Recommended action items")
    distribution_list: List[str] = dspy.OutputField(desc="Suggested distribution list")

reporting_agent = kaizen.create_agent("data_reporter", {
    "model": "gpt-4",
    "signature": DataReportingSignature,
    "data_integration": True,
    "visualization_generation": True
})
```

### Advanced RAG Patterns (5 patterns)

#### RAG1: Multi-Hop Reasoning RAG
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Complex queries requiring multiple retrieval steps

```python
# Future multi-hop RAG pattern
class MultiHopRAGSignature(dspy.Signature):
    """Multi-hop RAG for complex reasoning chains."""
    initial_query: str = dspy.InputField(desc="Starting query")
    max_hops: int = dspy.InputField(desc="Maximum retrieval hops", default=3)
    reasoning_strategy: str = dspy.InputField(desc="Strategy: breadth_first, depth_first")

    hop_queries: List[str] = dspy.OutputField(desc="Generated queries for each hop")
    hop_passages: List[List[str]] = dspy.OutputField(desc="Retrieved passages per hop")
    reasoning_chain: List[str] = dspy.OutputField(desc="Reasoning for each hop")
    final_answer: str = dspy.OutputField(desc="Synthesized final answer")
    evidence_strength: float = dspy.OutputField(desc="Overall evidence strength")

multi_hop_rag = kaizen.create_agent("multi_hop_rag", {
    "model": "gpt-4",
    "signature": MultiHopRAGSignature,
    "retrieval_strategy": "multi_hop",
    "knowledge_graph_integration": True
})
```

#### RAG2: Federated RAG
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Retrieval across multiple distributed knowledge sources

```python
# Future federated RAG pattern
class FederatedRAGSignature(dspy.Signature):
    """Federated RAG across multiple knowledge sources."""
    query: str = dspy.InputField(desc="Query for federated search")
    source_priorities: Dict[str, float] = dspy.InputField(desc="Source priority weights")
    fusion_strategy: str = dspy.InputField(desc="Fusion: weighted, ranked, consensus")

    source_results: Dict[str, List[str]] = dspy.OutputField(desc="Results per source")
    fusion_ranking: List[str] = dspy.OutputField(desc="Fused and ranked passages")
    source_attribution: List[str] = dspy.OutputField(desc="Source attribution per passage")
    federated_answer: str = dspy.OutputField(desc="Answer from federated sources")
    consensus_level: float = dspy.OutputField(desc="Cross-source consensus score")

federated_rag = kaizen.create_agent("federated_rag", {
    "model": "gpt-4",
    "signature": FederatedRAGSignature,
    "federated_sources": ["internal_kb", "web_search", "expert_db"],
    "fusion_algorithm": "rank_based_fusion"
})
```

#### RAG3: Graph-Enhanced RAG
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Knowledge graph traversal for contextual retrieval

```python
# Future graph RAG pattern
class GraphRAGSignature(dspy.Signature):
    """Graph-enhanced RAG with relationship traversal."""
    query: str = dspy.InputField(desc="Query requiring graph traversal")
    graph_context: str = dspy.InputField(desc="Knowledge graph context")
    traversal_depth: int = dspy.InputField(desc="Maximum traversal depth", default=2)

    relevant_entities: List[str] = dspy.OutputField(desc="Key entities identified")
    entity_relationships: List[Dict] = dspy.OutputField(desc="Entity relationships")
    traversal_path: List[str] = dspy.OutputField(desc="Graph traversal path")
    graph_answer: str = dspy.OutputField(desc="Answer based on graph knowledge")
    relationship_confidence: Dict = dspy.OutputField(desc="Confidence in relationships")

graph_rag = kaizen.create_agent("graph_rag", {
    "model": "gpt-4",
    "signature": GraphRAGSignature,
    "knowledge_graph": "neo4j",
    "entity_recognition": True,
    "relationship_weighting": True
})
```

#### RAG4: Self-Correcting RAG
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: RAG that improves retrieval based on answer quality

```python
# Future self-correcting RAG pattern
class SelfCorrectingRAGSignature(dspy.Signature):
    """Self-correcting RAG with quality feedback."""
    query: str = dspy.InputField(desc="User query")
    quality_threshold: float = dspy.InputField(desc="Minimum quality threshold", default=0.8)
    max_iterations: int = dspy.InputField(desc="Maximum correction iterations", default=3)

    initial_retrieval: List[str] = dspy.OutputField(desc="Initial retrieved passages")
    quality_assessment: float = dspy.OutputField(desc="Initial answer quality score")
    retrieval_refinements: List[str] = dspy.OutputField(desc="Retrieval refinements applied")
    corrected_answer: str = dspy.OutputField(desc="Quality-corrected answer")
    correction_summary: str = dspy.OutputField(desc="Summary of corrections made")

self_correcting_rag = kaizen.create_agent("self_correcting_rag", {
    "model": "gpt-4",
    "signature": SelfCorrectingRAGSignature,
    "quality_assessment": True,
    "iterative_refinement": True,
    "learning_enabled": True
})
```

#### RAG5: Agentic RAG
**Status**: 🟡 Planned
**Complexity**: Very High
**Use Case**: Autonomous RAG with tool integration and planning

```python
# Future agentic RAG pattern
class AgenticRAGSignature(dspy.Signature):
    """Agentic RAG with autonomous tool usage."""
    complex_query: str = dspy.InputField(desc="Complex query requiring autonomous planning")
    available_tools: List[str] = dspy.InputField(desc="Available RAG tools and sources")
    autonomy_level: str = dspy.InputField(desc="Low, medium, high autonomy")

    execution_plan: List[Dict] = dspy.OutputField(desc="Planned execution steps")
    tool_usage_log: List[Dict] = dspy.OutputField(desc="Log of tool usage")
    retrieved_information: Dict = dspy.OutputField(desc="Information from all sources")
    synthesized_answer: str = dspy.OutputField(desc="Final synthesized answer")
    autonomy_decisions: List[str] = dspy.OutputField(desc="Autonomous decisions made")

agentic_rag = kaizen.create_agent("agentic_rag", {
    "model": "gpt-4",
    "signature": AgenticRAGSignature,
    "autonomous_planning": True,
    "tool_integration": ["search", "calculate", "analyze"],
    "decision_making": True
})
```

### MCP Integration Patterns (5 patterns)

#### MCP1: Agent-as-MCP-Server
**Status**: ✅ Available (documented)
**Complexity**: Medium
**Use Case**: Exposing Kaizen agents as MCP servers

```python
# Complete Agent-as-MCP-Server pattern (see MCP documentation)
class AgentMCPServerSignature(dspy.Signature):
    """Agent exposed as MCP server."""
    client_request: str = dspy.InputField(desc="Request from MCP client")
    request_type: str = dspy.InputField(desc="Type: tool_call, resource_read")
    parameters: Dict = dspy.InputField(desc="Request parameters")

    mcp_response: str = dspy.OutputField(desc="MCP-formatted response")
    execution_metadata: Dict = dspy.OutputField(desc="Execution metadata")
    server_status: str = dspy.OutputField(desc="Server status information")

mcp_server_agent = kaizen.expose_as_mcp_server("research_agent", {
    "server_name": "kaizen-research-server",
    "capabilities": ["research", "analyze", "summarize"],
    "transport": "stdio"
})
```

#### MCP2: Agent-as-MCP-Client
**Status**: ✅ Available (documented)
**Complexity**: Medium
**Use Case**: Agents connecting to external MCP servers

```python
# Complete Agent-as-MCP-Client pattern (see MCP documentation)
class MCPClientSignature(dspy.Signature):
    """Agent enhanced with MCP client capabilities."""
    user_request: str = dspy.InputField(desc="User request")
    required_capabilities: List[str] = dspy.InputField(desc="Required MCP capabilities")

    tool_usage_plan: List[Dict] = dspy.OutputField(desc="Plan for MCP tool usage")
    mcp_results: Dict = dspy.OutputField(desc="Results from MCP tools")
    enhanced_response: str = dspy.OutputField(desc="Response enhanced with MCP tools")

mcp_enhanced_agent = kaizen.create_agent("mcp_enhanced", {
    "model": "gpt-4",
    "signature": MCPClientSignature,
    "mcp_capabilities": ["search", "calculate", "filesystem"],
    "auto_discovery": True
})
```

#### MCP3: Auto-Discovery Pattern
**Status**: ✅ Available (documented)
**Complexity**: High
**Use Case**: Automatic MCP server discovery and configuration

```python
# Zero-configuration MCP auto-discovery (see MCP documentation)
auto_discovery_agent = kaizen.create_smart_agent(
    name="auto_enhanced_agent",
    config={"model": "gpt-4"},
    capabilities=["search", "calculate", "analyze"]  # Auto-discovers MCP servers
)
```

#### MCP4: Multi-Server Orchestration
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Coordinating multiple MCP servers for complex tasks

```python
# Future multi-server orchestration pattern
class MCPOrchestrationSignature(dspy.Signature):
    """Multi-server MCP orchestration."""
    complex_task: str = dspy.InputField(desc="Task requiring multiple MCP servers")
    available_servers: List[str] = dspy.InputField(desc="Available MCP servers")
    orchestration_strategy: str = dspy.InputField(desc="Sequential, parallel, conditional")

    server_coordination_plan: List[Dict] = dspy.OutputField(desc="Server coordination plan")
    execution_sequence: List[str] = dspy.OutputField(desc="Server execution sequence")
    orchestrated_results: Dict = dspy.OutputField(desc="Results from all servers")
    coordination_summary: str = dspy.OutputField(desc="Orchestration summary")

mcp_orchestrator = kaizen.create_agent("mcp_orchestrator", {
    "model": "gpt-4",
    "signature": MCPOrchestrationSignature,
    "multi_server_coordination": True,
    "load_balancing": True
})
```

#### MCP5: Internal-External Coordination
**Status**: 🟡 Planned
**Complexity**: High
**Use Case**: Coordinating internal agents with external MCP services

```python
# Future internal-external coordination pattern
class InternalExternalCoordinationSignature(dspy.Signature):
    """Coordination between internal agents and external MCP services."""
    coordination_task: str = dspy.InputField(desc="Task requiring internal-external coordination")
    internal_capabilities: List[str] = dspy.InputField(desc="Internal agent capabilities")
    external_services: List[str] = dspy.InputField(desc="Available external MCP services")

    capability_mapping: Dict = dspy.OutputField(desc="Internal vs external capability mapping")
    coordination_strategy: str = dspy.OutputField(desc="Coordination strategy")
    execution_plan: List[Dict] = dspy.OutputField(desc="Detailed execution plan")
    fallback_plans: List[str] = dspy.OutputField(desc="Fallback plans for failures")

coordination_agent = kaizen.create_coordination_agent({
    "internal_agents": ["analyzer", "generator", "validator"],
    "external_mcp_services": ["search", "calculation", "data_processing"],
    "coordination_intelligence": True
})
```

## Pattern Composition and Integration

### Pattern Combination Strategies

#### Cross-Pattern Integration
```python
# Example: Combining multiple patterns for complex enterprise workflow
class EnterpriseWorkflowOrchestrator:
    """Orchestrate multiple patterns for enterprise workflows."""

    def __init__(self):
        # Single-agent patterns
        self.document_processor = self.create_document_agent()
        self.research_assistant = self.create_research_agent()

        # Multi-agent patterns
        self.approval_workflow = self.create_approval_system()
        self.specialist_team = self.create_specialist_system()

        # Enterprise patterns
        self.compliance_monitor = self.create_compliance_agent()
        self.reporting_system = self.create_reporting_system()

        # MCP integration
        self.mcp_coordinator = self.setup_mcp_integration()

    def execute_enterprise_workflow(self, task):
        """Execute complex enterprise workflow using multiple patterns."""
        # Pattern coordination logic
        pass
```

#### Pattern Selection Framework
```python
class PatternSelector:
    """Framework for selecting appropriate patterns based on task characteristics."""

    def select_patterns(self, task_description, requirements):
        """Select optimal patterns for task requirements."""
        pattern_recommendations = {
            "single_agent": self.evaluate_single_agent_patterns(task_description),
            "multi_agent": self.evaluate_multi_agent_patterns(requirements),
            "enterprise": self.evaluate_enterprise_patterns(requirements),
            "rag": self.evaluate_rag_patterns(task_description),
            "mcp": self.evaluate_mcp_patterns(requirements)
        }

        return self.optimize_pattern_combination(pattern_recommendations)
```

---

**📋 Complete Patterns Catalog**: This comprehensive catalog provides all 34 proven agent design patterns for Kaizen development. These patterns cover the full spectrum from simple single-agent implementations to complex enterprise multi-agent orchestrations, providing building blocks for robust, scalable AI applications across all use cases.
