# BMAD Context Management Engine

## Overview

The Context Management Engine provides sophisticated context preservation, sharing, and persistence across persona interactions, ensuring seamless continuity and intelligent context-aware decision making throughout the BMAD Method workflow.

## Core Architecture

###  Context Data Model

#### Hierarchical Context Structure
\```yaml
context_hierarchy:
  global_context:
    description: "System-wide context shared across all interactions"
    components:
      - "user_preferences"
      - "system_configuration"
      - "global_constraints"
      - "organizational_standards"
    
  project_context:
    description: "Project-specific context shared within project scope"
    components:
      - "project_goals_and_objectives"
      - "stakeholder_information"
      - "technical_requirements"
      - "timeline_and_milestones"
      - "budget_and_resource_constraints"
      - "quality_standards"
    
  session_context:
    description: "Session-specific context for current interaction"
    components:
      - "current_conversation_history"
      - "active_personas"
      - "workflow_state"
      - "pending_decisions"
      - "temporary_artifacts"
    
  persona_context:
    description: "Persona-specific context and working memory"
    components:
      - "persona_working_memory"
      - "specialized_knowledge_cache"
      - "persona_specific_preferences"
      - "task_specific_context"
      - "collaboration_history"
```

#### Context Entity Schema
\```json
{
  "context_entity": {
    "id": "unique_context_identifier",
    "type": "global|project|session|persona",
    "scope": "context_scope_definition",
    "created_at": "timestamp",
    "updated_at": "timestamp",
    "version": "context_version_number",
    "metadata": {
      "owner": "context_owner",
      "access_level": "public|private|restricted",
      "retention_policy": "retention_duration",
      "encryption_level": "none|standard|high"
    },
    "content": {
      "structured_data": {},
      "unstructured_data": "",
      "relationships": [],
      "tags": [],
      "priority": "high|medium|low"
    },
    "access_control": {
      "read_permissions": [],
      "write_permissions": [],
      "share_permissions": []
    }
  }
}
```

###  Context Lifecycle Management

#### Context Creation and Initialization
```python
def create_context(context_type, scope, initial_data, metadata=None):
    """
    Create new context entity with proper initialization
    """
    
    context_id = generate_context_id(context_type, scope)
    
    context_entity = {
        'id': context_id,
        'type': context_type,
        'scope': scope,
        'created_at': get_current_timestamp(),
        'updated_at': get_current_timestamp(),
        'version': 1,
        'metadata': initialize_metadata(metadata),
        'content': structure_initial_data(initial_data),
        'access_control': initialize_access_control(context_type)
    }
    
    # Validate context structure
    validate_context_structure(context_entity)
    
    # Store context
    store_context(context_entity)
    
    # Initialize relationships
    initialize_context_relationships(context_entity)
    
    return context_id

def initialize_metadata(metadata):
    """Initialize context metadata with defaults"""
    
    default_metadata = {
        'owner': get_current_user(),
        'access_level': 'private',
        'retention_policy': '30_days',
        'encryption_level': 'standard'
    }
    
    if metadata:
        default_metadata.update(metadata)
    
    return default_metadata
```

#### Context Update and Versioning
\```yaml
versioning_strategy:
  version_control:
    strategy: "semantic_versioning"
    major_changes: "structural_modifications"
    minor_changes: "content_additions"
    patch_changes: "content_updates"
    
  change_tracking:
    track_changes: true
    change_log_retention: "90_days"
    diff_calculation: "intelligent_diff"
    
  conflict_resolution:
    concurrent_updates: "last_writer_wins_with_merge"
    conflict_detection: "content_hash_comparison"
    merge_strategy: "intelligent_merge_with_user_review"
    
  rollback_capability:
    rollback_support: true
    rollback_window: "7_days"
    rollback_granularity: "field_level"
```

###  Context Sharing and Synchronization

#### Intelligent Context Sharing Algorithm
```python
def share_context_between_personas(source_persona, target_persona, context_filter=None):
    """
    Intelligently share relevant context between personas
    """
    
    # Get source persona context
    source_context = get_persona_context(source_persona)
    
    # Analyze target persona requirements
    target_requirements = analyze_persona_context_needs(target_persona)
    
    # Filter relevant context
    relevant_context = filter_relevant_context(
        source_context, 
        target_requirements, 
        context_filter
    )
    
    # Apply privacy and security filters
    filtered_context = apply_privacy_filters(relevant_context, target_persona)
    
    # Transform context for target persona
    transformed_context = transform_context_for_persona(filtered_context, target_persona)
    
    # Validate context compatibility
    validate_context_compatibility(transformed_context, target_persona)
    
    # Transfer context
    transfer_context(transformed_context, target_persona)
    
    # Log context sharing
    log_context_sharing(source_persona, target_persona, transformed_context)
    
    return {
        'transfer_successful': True,
        'context_items_transferred': len(transformed_context),
        'transfer_timestamp': get_current_timestamp()
    }

def filter_relevant_context(source_context, target_requirements, context_filter):
    """Filter context based on relevance and requirements"""
    
    relevance_scores = {}
    
    for context_item in source_context:
        # Calculate relevance score
        relevance_score = calculate_context_relevance(context_item, target_requirements)
        
        # Apply custom filters if provided
        if context_filter:
            relevance_score = apply_custom_filter(relevance_score, context_item, context_filter)
        
        # Include if above threshold
        if relevance_score >= get_relevance_threshold():
            relevance_scores[context_item.id] = relevance_score
    
    # Sort by relevance and return top items
    sorted_items = sorted(relevance_scores.items(), key=lambda x: x[1], reverse=True)
    
    return [get_context_item(item_id) for item_id, score in sorted_items]
```

#### Context Synchronization Strategies
\```yaml
synchronization_patterns:
  real_time_sync:
    description: "Immediate context synchronization"
    use_cases: ["critical_decisions", "urgent_handoffs", "error_conditions"]
    latency_target: "< 100ms"
    
  batch_sync:
    description: "Periodic batch synchronization"
    use_cases: ["routine_updates", "background_processing", "optimization"]
    frequency: "every_5_minutes"
    
  event_driven_sync:
    description: "Synchronization triggered by specific events"
    use_cases: ["persona_switches", "milestone_completion", "context_changes"]
    trigger_events: ["persona_handoff", "workflow_transition", "user_action"]
    
  lazy_sync:
    description: "On-demand synchronization when context is accessed"
    use_cases: ["infrequent_access", "large_context_sets", "resource_optimization"]
    cache_strategy: "intelligent_prefetching"
```

###  Context Persistence and Storage

#### Multi-Tier Storage Architecture
\```yaml
storage_tiers:
  hot_storage:
    description: "Frequently accessed context in memory"
    technology: "redis_cluster"
    capacity: "active_session_context"
    access_time: "< 10ms"
    retention: "session_duration"
    
  warm_storage:
    description: "Recently accessed context in fast storage"
    technology: "ssd_database"
    capacity: "recent_project_context"
    access_time: "< 50ms"
    retention: "30_days"
    
  cold_storage:
    description: "Archived context in cost-effective storage"
    technology: "object_storage"
    capacity: "historical_context"
    access_time: "< 500ms"
    retention: "1_year"
    
  archive_storage:
    description: "Long-term archived context"
    technology: "compressed_archive"
    capacity: "compliance_retention"
    access_time: "< 5_seconds"
    retention: "7_years"
```

#### Context Persistence Strategies
```python
def persist_context(context_entity, persistence_level='standard'):
    """
    Persist context with appropriate storage strategy
    """
    
    # Determine storage tier based on context type and access patterns
    storage_tier = determine_storage_tier(context_entity, persistence_level)
    
    # Prepare context for storage
    prepared_context = prepare_context_for_storage(context_entity, storage_tier)
    
    # Apply compression if appropriate
    if should_compress_context(prepared_context, storage_tier):
        prepared_context = compress_context(prepared_context)
    
    # Apply encryption based on sensitivity
    if requires_encryption(context_entity):
        prepared_context = encrypt_context(prepared_context, context_entity.metadata.encryption_level)
    
    # Store in appropriate tier
    storage_result = store_in_tier(prepared_context, storage_tier)
    
    # Update context index
    update_context_index(context_entity.id, storage_tier, storage_result.location)
    
    # Set up retention policy
    schedule_retention_policy(context_entity.id, context_entity.metadata.retention_policy)
    
    return {
        'persistence_successful': True,
        'storage_tier': storage_tier,
        'storage_location': storage_result.location,
        'compression_applied': prepared_context.compressed,
        'encryption_applied': prepared_context.encrypted
    }

def retrieve_context(context_id, access_pattern='standard'):
    """
    Retrieve context with intelligent caching and prefetching
    """
    
    # Check hot cache first
    cached_context = check_hot_cache(context_id)
    if cached_context:
        update_access_statistics(context_id, 'cache_hit')
        return cached_context
    
    # Locate context in storage tiers
    storage_location = locate_context_in_storage(context_id)
    
    # Retrieve from appropriate tier
    stored_context = retrieve_from_storage(storage_location)
    
    # Decrypt if necessary
    if stored_context.encrypted:
        stored_context = decrypt_context(stored_context)
    
    # Decompress if necessary
    if stored_context.compressed:
        stored_context = decompress_context(stored_context)
    
    # Cache in hot storage for future access
    cache_in_hot_storage(context_id, stored_context)
    
    # Prefetch related context if appropriate
    if should_prefetch_related_context(context_id, access_pattern):
        prefetch_related_context(context_id)
    
    # Update access statistics
    update_access_statistics(context_id, 'storage_retrieval')
    
    return stored_context
```

###  Privacy and Security Framework

#### Access Control and Permissions
\```yaml
access_control_model:
  role_based_access:
    roles:
      - "context_owner"
      - "project_member"
      - "persona_user"
      - "system_administrator"
      - "auditor"
    
    permissions:
      read: ["view_context_content", "access_context_metadata"]
      write: ["modify_context_content", "update_context_metadata"]
      share: ["grant_access_to_others", "create_context_links"]
      delete: ["remove_context", "purge_context_history"]
      admin: ["manage_access_control", "configure_retention_policies"]
    
  attribute_based_access:
    attributes:
      - "context_sensitivity_level"
      - "user_clearance_level"
      - "project_membership"
      - "persona_authorization"
      - "time_based_restrictions"
    
    policies:
      - "high_sensitivity_requires_high_clearance"
      - "project_context_requires_project_membership"
      - "persona_context_requires_persona_authorization"
      - "time_restricted_context_enforces_time_limits"
```

#### Data Privacy and Compliance
```python
def apply_privacy_protection(context_entity, target_persona):
    """
    Apply privacy protection based on context sensitivity and target persona
    """
    
    # Assess context sensitivity
    sensitivity_level = assess_context_sensitivity(context_entity)
    
    # Get target persona clearance
    persona_clearance = get_persona_clearance_level(target_persona)
    
    # Apply privacy filters
    if sensitivity_level > persona_clearance:
        # Redact sensitive information
        filtered_context = redact_sensitive_information(context_entity, sensitivity_level, persona_clearance)
    else:
        filtered_context = context_entity
    
    # Apply data minimization
    minimized_context = apply_data_minimization(filtered_context, target_persona)
    
    # Log privacy protection actions
    log_privacy_protection(context_entity.id, target_persona, sensitivity_level, persona_clearance)
    
    return minimized_context

def redact_sensitive_information(context_entity, sensitivity_level, clearance_level):
    """Redact information based on sensitivity and clearance levels"""
    
    redaction_rules = {
        'personal_data': ['names', 'emails', 'phone_numbers', 'addresses'],
        'financial_data': ['budgets', 'costs', 'revenue_projections'],
        'technical_secrets': ['api_keys', 'passwords', 'proprietary_algorithms'],
        'business_confidential': ['strategic_plans', 'competitive_analysis', 'internal_processes']
    }
    
    redacted_context = copy.deepcopy(context_entity)
    
    for data_type, fields in redaction_rules.items():
        if should_redact_data_type(data_type, sensitivity_level, clearance_level):
            redacted_context = redact_fields(redacted_context, fields)
    
    return redacted_context
```

###  Performance Optimization

#### Intelligent Caching Strategy
\```yaml
caching_strategies:
  multi_level_cache:
    l1_cache:
      description: "In-memory cache for immediate access"
      technology: "application_memory"
      capacity: "100MB"
      ttl: "5_minutes"
      
    l2_cache:
      description: "Distributed cache for shared access"
      technology: "redis_cluster"
      capacity: "1GB"
      ttl: "30_minutes"
      
    l3_cache:
      description: "Persistent cache for warm data"
      technology: "ssd_cache"
      capacity: "10GB"
      ttl: "24_hours"
  
  cache_policies:
    eviction_policy: "lru_with_priority_boost"
    prefetch_strategy: "predictive_prefetching"
    invalidation_strategy: "smart_invalidation"
    
  cache_optimization:
    compression: "context_aware_compression"
    serialization: "efficient_binary_serialization"
    partitioning: "context_type_based_partitioning"
```

#### Context Retrieval Optimization
```python
def optimize_context_retrieval(context_query, performance_target='standard'):
    """
    Optimize context retrieval based on query patterns and performance targets
    """
    
    # Analyze query pattern
    query_analysis = analyze_context_query(context_query)
    
    # Determine optimization strategy
    optimization_strategy = determine_optimization_strategy(query_analysis, performance_target)
    
    # Apply query optimization
    optimized_query = apply_query_optimization(context_query, optimization_strategy)
    
    # Execute with performance monitoring
    start_time = get_current_timestamp()
    
    if optimization_strategy.use_parallel_retrieval:
        results = execute_parallel_retrieval(optimized_query)
    else:
        results = execute_sequential_retrieval(optimized_query)
    
    end_time = get_current_timestamp()
    retrieval_time = end_time - start_time
    
    # Apply result optimization
    optimized_results = optimize_results(results, query_analysis)
    
    # Update performance metrics
    update_performance_metrics(context_query, retrieval_time, len(optimized_results))
    
    # Learn from performance
    learn_from_retrieval_performance(context_query, optimization_strategy, retrieval_time)
    
    return {
        'results': optimized_results,
        'retrieval_time': retrieval_time,
        'optimization_applied': optimization_strategy,
        'performance_target_met': retrieval_time <= get_performance_target(performance_target)
    }
```

###  Context Intelligence and Learning

#### Context Pattern Recognition
\```yaml
pattern_recognition:
  usage_patterns:
    frequent_access_patterns: "identify_commonly_accessed_context_combinations"
    temporal_patterns: "recognize_time_based_context_usage"
    persona_patterns: "learn_persona_specific_context_preferences"
    workflow_patterns: "understand_context_flow_in_workflows"
    
  optimization_opportunities:
    prefetch_candidates: "contexts_likely_to_be_accessed_together"
    cache_optimization: "contexts_that_benefit_from_longer_caching"
    compression_candidates: "contexts_suitable_for_compression"
    archival_candidates: "contexts_ready_for_archival"
    
  anomaly_detection:
    unusual_access_patterns: "detect_suspicious_context_access"
    performance_anomalies: "identify_performance_degradation"
    data_integrity_issues: "detect_context_corruption_or_inconsistency"
```

#### Adaptive Context Management
```python
def adapt_context_management(usage_statistics, performance_metrics, user_feedback):
    """
    Adapt context management strategies based on learning
    """
    
    # Analyze usage patterns
    usage_patterns = analyze_usage_patterns(usage_statistics)
    
    # Identify optimization opportunities
    optimization_opportunities = identify_optimization_opportunities(
        usage_patterns, 
        performance_metrics
    )
    
    # Generate adaptation recommendations
    adaptations = generate_adaptation_recommendations(
        optimization_opportunities, 
        user_feedback
    )
    
    # Apply safe adaptations automatically
    safe_adaptations = filter_safe_adaptations(adaptations)
    apply_adaptations(safe_adaptations)
    
    # Queue risky adaptations for review
    risky_adaptations = filter_risky_adaptations(adaptations)
    queue_for_review(risky_adaptations)
    
    # Monitor adaptation impact
    monitor_adaptation_impact(safe_adaptations)
    
    return {
        'adaptations_applied': len(safe_adaptations),
        'adaptations_queued': len(risky_adaptations),
        'expected_performance_improvement': estimate_performance_improvement(adaptations)
    }
```

###  Monitoring and Analytics

#### Context Health Monitoring
\```yaml
health_metrics:
  availability_metrics:
    context_availability: "> 99.9%"
    retrieval_success_rate: "> 99.5%"
    storage_reliability: "> 99.99%"
    
  performance_metrics:
    average_retrieval_time: "< 100ms"
    95th_percentile_retrieval_time: "< 200ms"
    cache_hit_ratio: "> 80%"
    
  quality_metrics:
    context_accuracy: "> 95%"
    context_completeness: "> 90%"
    context_relevance: "> 85%"
    
  security_metrics:
    unauthorized_access_attempts: "< 0.1%"
    privacy_violations: "0"
    data_breach_incidents: "0"
```

#### Analytics and Insights
```python
def generate_context_analytics(time_period='last_30_days'):
    """
    Generate comprehensive analytics on context usage and performance
    """
    
    # Collect metrics data
    usage_data = collect_usage_metrics(time_period)
    performance_data = collect_performance_metrics(time_period)
    quality_data = collect_quality_metrics(time_period)
    
    # Generate insights
    insights = {
        'usage_insights': analyze_usage_trends(usage_data),
        'performance_insights': analyze_performance_trends(performance_data),
        'quality_insights': analyze_quality_trends(quality_data),
        'optimization_recommendations': generate_optimization_recommendations(
            usage_data, performance_data, quality_data
        )
    }
    
    # Create visualizations
    visualizations = create_analytics_visualizations(insights)
    
    # Generate reports
    reports = generate_analytics_reports(insights, visualizations)
    
    return {
        'insights': insights,
        'visualizations': visualizations,
        'reports': reports,
        'generated_at': get_current_timestamp()
    }
```
