# BMAD Intelligent Routing Engine

## Overview

The Intelligent Routing Engine automatically analyzes user requests and routes them to the most appropriate persona(s) based on context, complexity, and required capabilities. It provides transparent decision-making with fallback mechanisms and continuous learning.

## Core Components

###  Request Analysis Engine

#### Natural Language Processing Pipeline
\```yaml
nlp_pipeline:
  text_preprocessing:
    - "tokenization"
    - "stop_word_removal"
    - "lemmatization"
    - "named_entity_recognition"
    
  feature_extraction:
    - "keyword_extraction"
    - "intent_classification"
    - "complexity_assessment"
    - "domain_identification"
    
  context_analysis:
    - "project_phase_detection"
    - "technology_stack_identification"
    - "urgency_assessment"
    - "scope_evaluation"
```

#### Request Classification Framework
\```yaml
classification_categories:
  primary_domains:
    architecture: ["system_design", "technical_architecture", "scalability", "integration"]
    development: ["coding", "implementation", "debugging", "testing"]
    design: ["ui_ux", "visual_design", "user_experience", "prototyping"]
    management: ["project_planning", "requirements", "coordination", "process"]
    documentation: ["technical_writing", "api_docs", "guides", "procedures"]
    quality: ["code_review", "performance", "security", "optimization"]
    
  complexity_levels:
    simple: ["single_persona", "straightforward_task", "well_defined_scope"]
    moderate: ["multiple_considerations", "some_ambiguity", "cross_functional"]
    complex: ["multiple_personas", "high_ambiguity", "enterprise_scope"]
    expert: ["specialized_knowledge", "critical_decisions", "high_impact"]
    
  urgency_indicators:
    low: ["research", "planning", "documentation", "optimization"]
    medium: ["feature_development", "enhancement", "review"]
    high: ["bug_fix", "security_issue", "production_problem"]
    critical: ["system_down", "data_breach", "compliance_violation"]
```

###  Persona Matching Algorithm

#### Capability-Based Matching
```python
def calculate_persona_match_score(request_features, persona_capabilities):
    """
    Calculate match score between request and persona capabilities
    Returns score between 0.0 and 1.0
    """
    
    # Primary capability matching (60% weight)
    primary_match = calculate_primary_capability_match(
        request_features.primary_domain,
        persona_capabilities.primary_specializations
    )
    
    # Secondary capability matching (25% weight)
    secondary_match = calculate_secondary_capability_match(
        request_features.secondary_domains,
        persona_capabilities.secondary_specializations
    )
    
    # Technology stack alignment (10% weight)
    tech_match = calculate_technology_alignment(
        request_features.technology_stack,
        persona_capabilities.technology_expertise
    )
    
    # Complexity level compatibility (5% weight)
    complexity_match = calculate_complexity_compatibility(
        request_features.complexity_level,
        persona_capabilities.complexity_handling
    )
    
    # Calculate weighted score
    total_score = (
        primary_match * 0.60 +
        secondary_match * 0.25 +
        tech_match * 0.10 +
        complexity_match * 0.05
    )
    
    return min(total_score, 1.0)
```

#### Multi-Persona Coordination Logic
\```yaml
coordination_strategies:
  sequential_workflow:
    description: "Personas work in sequence with handoffs"
    use_cases: ["project_initiation", "feature_development", "documentation_creation"]
    coordination: "structured_handoff_protocols"
    
  parallel_collaboration:
    description: "Multiple personas work simultaneously"
    use_cases: ["quality_validation", "comprehensive_review", "multi_domain_analysis"]
    coordination: "shared_context_and_synchronization"
    
  hierarchical_consultation:
    description: "Primary persona with specialist consultations"
    use_cases: ["complex_troubleshooting", "enterprise_architecture", "security_review"]
    coordination: "primary_lead_with_expert_input"
    
  dynamic_handoff:
    description: "Adaptive persona switching based on evolving needs"
    use_cases: ["exploratory_analysis", "iterative_development", "problem_solving"]
    coordination: "context_aware_transitions"
```

###  Routing Decision Engine

#### Decision Tree Algorithm
\```yaml
routing_decision_tree:
  root_analysis:
    question: "What is the primary intent of the request?"
    branches:
      create_new: "creation_workflow"
      analyze_existing: "analysis_workflow"
      optimize_improve: "optimization_workflow"
      troubleshoot_fix: "troubleshooting_workflow"
      document_explain: "documentation_workflow"
      
  creation_workflow:
    question: "What type of artifact needs to be created?"
    branches:
      project_requirements: "analyst -> product_manager"
      technical_architecture: "architect -> design_architect"
      user_interface: "design_architect -> v0_ux_ui_architect"
      documentation: "technical_documentation_architect"
      
  analysis_workflow:
    question: "What domain requires analysis?"
    branches:
      performance: "performance_optimization_specialist"
      security: "security_integration_specialist"
      code_quality: "polyglot_code_review_specialist"
      enterprise_strategy: "enterprise_architecture_consultant"
      
  optimization_workflow:
    question: "What aspect needs optimization?"
    branches:
      system_performance: "performance_optimization_specialist -> architect"
      security_posture: "security_integration_specialist -> architect"
      code_quality: "polyglot_code_review_specialist"
      workflow_process: "scrum_master -> product_owner"
      
  troubleshooting_workflow:
    question: "What is the complexity of the issue?"
    branches:
      simple_bug: "advanced_troubleshooting_specialist"
      performance_issue: "performance_optimization_specialist -> advanced_troubleshooting_specialist"
      security_incident: "security_integration_specialist -> advanced_troubleshooting_specialist"
      system_failure: "advanced_troubleshooting_specialist -> architect -> enterprise_architecture_consultant"
```

#### Confidence Scoring System
\```yaml
confidence_metrics:
  high_confidence: 
    threshold: "> 0.85"
    action: "direct_routing"
    validation: "minimal_user_confirmation"
    
  medium_confidence:
    threshold: "0.65 - 0.85"
    action: "routing_with_alternatives"
    validation: "present_options_to_user"
    
  low_confidence:
    threshold: "0.45 - 0.65"
    action: "guided_selection"
    validation: "interactive_clarification"
    
  very_low_confidence:
    threshold: "< 0.45"
    action: "fallback_to_orchestrator"
    validation: "manual_persona_selection"
```

## Routing Algorithms

###  Primary Routing Strategies

#### Intent-Based Routing
```python
def route_by_intent(request_text, context):
    """
    Route based on detected user intent and context
    """
    
    # Extract intent from request
    intent = extract_intent(request_text)
    
    # Analyze context for additional routing hints
    context_hints = analyze_context(context)
    
    # Map intent to persona capabilities
    persona_candidates = map_intent_to_personas(intent, context_hints)
    
    # Score and rank candidates
    ranked_personas = score_and_rank_personas(persona_candidates, request_text)
    
    # Apply confidence thresholds
    routing_decision = apply_confidence_thresholds(ranked_personas)
    
    return routing_decision

def extract_intent(request_text):
    """Extract primary intent from user request"""
    intent_patterns = {
        'create': ['create', 'build', 'develop', 'design', 'implement'],
        'analyze': ['analyze', 'review', 'assess', 'evaluate', 'examine'],
        'optimize': ['optimize', 'improve', 'enhance', 'refactor', 'tune'],
        'troubleshoot': ['debug', 'fix', 'resolve', 'troubleshoot', 'diagnose'],
        'document': ['document', 'explain', 'describe', 'guide', 'manual']
    }
    
    # Implementation details for intent extraction
    return detected_intent
```

#### Technology Stack Routing
\```yaml
technology_routing_rules:
  frontend_technologies:
    react_typescript:
      primary: ["v0_ux_ui_architect", "design_architect"]
      secondary: ["performance_optimization_specialist", "polyglot_code_review_specialist"]
      
    vue_angular:
      primary: ["design_architect", "architect"]
      secondary: ["cross_platform_integration_specialist"]
      
  backend_technologies:
    nodejs:
      primary: ["architect", "cross_platform_integration_specialist"]
      secondary: ["performance_optimization_specialist", "security_integration_specialist"]
      
    aspnet:
      primary: ["architect", "enterprise_architecture_consultant"]
      secondary: ["security_integration_specialist", "performance_optimization_specialist"]
      
    python:
      primary: ["architect", "performance_optimization_specialist"]
      secondary: ["advanced_troubleshooting_specialist", "polyglot_code_review_specialist"]
      
  infrastructure_technologies:
    cloud_platforms:
      primary: ["devops_documentation_specialist", "architect"]
      secondary: ["security_integration_specialist", "enterprise_architecture_consultant"]
      
    containerization:
      primary: ["devops_documentation_specialist", "cross_platform_integration_specialist"]
      secondary: ["performance_optimization_specialist", "security_integration_specialist"]
```

#### Context-Aware Routing
\```yaml
context_routing_factors:
  project_phase:
    initiation:
      primary_personas: ["analyst", "product_manager"]
      workflow: "sequential"
      
    planning:
      primary_personas: ["architect", "design_architect"]
      secondary_personas: ["enterprise_architecture_consultant", "security_integration_specialist"]
      workflow: "parallel_consultation"
      
    development:
      primary_personas: ["v0_ux_ui_architect", "scrum_master"]
      quality_personas: ["polyglot_code_review_specialist", "performance_optimization_specialist"]
      workflow: "development_with_validation"
      
    deployment:
      primary_personas: ["devops_documentation_specialist"]
      support_personas: ["security_integration_specialist", "advanced_troubleshooting_specialist"]
      workflow: "deployment_with_monitoring"
      
  urgency_level:
    critical:
      routing_strategy: "immediate_expert_routing"
      personas: ["advanced_troubleshooting_specialist", "security_integration_specialist"]
      escalation: ["enterprise_architecture_consultant", "architect"]
      
    high:
      routing_strategy: "fast_track_routing"
      validation: "minimal"
      
    normal:
      routing_strategy: "standard_routing"
      validation: "comprehensive"
      
    low:
      routing_strategy: "optimized_routing"
      validation: "thorough_analysis"
```

## Performance Optimization

###  Routing Performance Targets
\```yaml
performance_requirements:
  routing_decision_time: "< 500ms"
  persona_activation_time: "< 1000ms"
  context_analysis_time: "< 200ms"
  confidence_calculation_time: "< 100ms"
  
optimization_strategies:
  caching:
    request_patterns: "cache_common_routing_decisions"
    persona_capabilities: "cache_persona_metadata"
    context_analysis: "cache_context_analysis_results"
    
  parallel_processing:
    capability_matching: "parallel_persona_scoring"
    context_analysis: "concurrent_context_processing"
    confidence_calculation: "parallel_confidence_scoring"
    
  algorithmic_optimization:
    early_termination: "stop_analysis_when_high_confidence_reached"
    pruning: "eliminate_low_probability_personas_early"
    approximation: "use_approximation_for_non_critical_calculations"
```

###  Continuous Learning System
\```yaml
learning_mechanisms:
  feedback_collection:
    user_satisfaction: "collect_user_feedback_on_routing_decisions"
    routing_accuracy: "track_successful_vs_failed_routings"
    performance_metrics: "monitor_routing_performance_continuously"
    
  pattern_recognition:
    successful_patterns: "identify_high_performing_routing_patterns"
    failure_analysis: "analyze_failed_routing_decisions"
    user_preferences: "learn_individual_user_routing_preferences"
    
  model_improvement:
    weight_adjustment: "adjust_scoring_weights_based_on_feedback"
    threshold_optimization: "optimize_confidence_thresholds"
    algorithm_refinement: "improve_routing_algorithms_based_on_data"
    
  adaptation_strategies:
    real_time_learning: "adapt_routing_decisions_based_on_immediate_feedback"
    batch_learning: "periodic_model_updates_based_on_accumulated_data"
    a_b_testing: "test_new_routing_strategies_with_subset_of_users"
```

## Error Handling and Fallback

###  Robust Error Handling
\```yaml
error_scenarios:
  routing_failures:
    no_suitable_persona:
      action: "fallback_to_orchestrator_guidance"
      message: "Let me help you find the right expertise for your request"
      
    multiple_equal_scores:
      action: "present_options_to_user"
      message: "I found multiple experts who could help. Which would you prefer?"
      
    low_confidence_routing:
      action: "guided_clarification"
      message: "To route you to the best expert, could you clarify..."
      
  performance_issues:
    routing_timeout:
      action: "fallback_to_simple_routing"
      timeout_threshold: "500ms"
      
    persona_unavailable:
      action: "suggest_alternative_persona"
      alternatives: "ranked_by_capability_similarity"
      
    system_overload:
      action: "queue_request_with_estimated_wait_time"
      priority: "based_on_urgency_level"
      
  data_quality_issues:
    incomplete_request:
      action: "interactive_request_completion"
      guidance: "structured_question_prompts"
      
    ambiguous_context:
      action: "context_clarification_workflow"
      clarification: "targeted_questions_based_on_ambiguity"
```

###  Fallback Strategies
\```yaml
fallback_hierarchy:
  level_1_intelligent_routing:
    description: "Full AI-powered routing with high confidence"
    success_rate: "> 85%"
    
  level_2_guided_routing:
    description: "AI suggestions with user confirmation"
    fallback_trigger: "medium_confidence_or_user_preference"
    
  level_3_manual_selection:
    description: "User selects from recommended personas"
    fallback_trigger: "low_confidence_or_routing_failure"
    
  level_4_orchestrator_guidance:
    description: "BMad orchestrator provides guidance"
    fallback_trigger: "complete_routing_failure"
    
  level_5_default_workflow:
    description: "Standard workflow based on request type"
    fallback_trigger: "system_unavailability"
```

## Integration and Validation

###  Orchestrator Integration
\```yaml
integration_points:
  orchestrator_commands:
    "/route-analyze": "analyze_request_and_show_routing_decision"
    "/route-explain": "explain_why_specific_persona_was_selected"
    "/route-alternatives": "show_alternative_persona_options"
    "/route-feedback": "collect_feedback_on_routing_decision"
    
  workflow_integration:
    pre_routing: "context_analysis_and_preparation"
    routing_decision: "intelligent_persona_selection"
    post_routing: "validation_and_feedback_collection"
    
  quality_validation:
    routing_accuracy: "track_successful_routing_percentage"
    user_satisfaction: "measure_user_satisfaction_with_routing"
    performance_metrics: "monitor_routing_performance_continuously"
```

###  Validation Framework
\```yaml
validation_metrics:
  accuracy_metrics:
    routing_success_rate: "> 90%"
    user_satisfaction_score: "> 4.5/5"
    first_attempt_success: "> 85%"
    
  performance_metrics:
    average_routing_time: "< 300ms"
    95th_percentile_routing_time: "< 500ms"
    system_availability: "> 99.5%"
    
  quality_metrics:
    routing_explanation_clarity: "> 4.0/5"
    alternative_suggestion_relevance: "> 4.0/5"
    fallback_effectiveness: "> 80%"
    
validation_methods:
  automated_testing:
    unit_tests: "test_individual_routing_components"
    integration_tests: "test_end_to_end_routing_workflows"
    performance_tests: "validate_routing_performance_requirements"
    
  user_acceptance_testing:
    scenario_testing: "test_routing_with_realistic_user_scenarios"
    usability_testing: "validate_routing_user_experience"
    feedback_collection: "gather_user_feedback_on_routing_decisions"
    
  continuous_monitoring:
    real_time_metrics: "monitor_routing_performance_in_production"
    error_tracking: "track_and_analyze_routing_errors"
    user_behavior_analysis: "analyze_user_interaction_patterns"
```
