# AI Context Orchestration - Business Requirements

**Document Version**: 1.0
**Date**: January 2025
**Status**: Business Requirements Specification
**Module**: AI Context Orchestration (`pkg/ai/holmesgpt/`, `pkg/api/context/`)

---

## 1. Purpose & Scope

### 1.1 Business Purpose
The AI Context Orchestration components provide **historical and organizational intelligence** for AI-driven investigations. This capability complements HolmesGPT's real-time data gathering toolsets by providing historical patterns, organizational context, and trend analysis that guide investigation strategy and decision-making.

**Critical Architectural Distinction**:
- **HolmesGPT Toolsets** (kubernetes, prometheus): Real-time operational data (current logs, events, metrics)
- **Context API Service**: Historical intelligence and organizational context (NOT current/real-time data)

This separation enables efficient dual-source intelligence gathering: HolmesGPT answers "What's happening NOW?" while Context API answers "What happened BEFORE?" and "What's the organizational context?"

### 1.2 Scope
- **Dynamic Context Orchestration**: AI-driven context data retrieval based on investigation needs
- **Context API Integration**: RESTful endpoints for real-time context access
- **HolmesGPT Toolset Integration**: Custom toolsets enabling HolmesGPT to orchestrate context gathering
- **Dynamic Toolset Configuration**: Automatic toolset configuration based on deployed cluster services
- **Performance Optimization**: Reduced payload sizes and improved investigation efficiency
- **Architectural Evolution**: Migration from static pre-enrichment to dynamic orchestration

### 1.3 Business Value
- **40-60% improvement** in investigation efficiency through targeted context gathering
- **50-70% reduction** in network payload sizes and memory utilization
- **15-25% improvement** in context relevance and investigation accuracy
- **Zero-configuration adaptability** to cluster environments through dynamic service discovery
- **30-50% reduction** in setup complexity through automatic toolset configuration
- **Enhanced scalability** for complex investigation scenarios
- **Future-proof architecture** for AI-driven troubleshooting evolution

---

## 2. Dynamic Context Orchestration

### 2.1 Business Capabilities

#### 2.1.1 Intelligent Context Discovery
- **BR-CONTEXT-001**: MUST enable AI services to dynamically discover available context types for investigation scenarios
  - **Enhanced**: Include alert tracking ID in all context requests and responses
  - **Enhanced**: Correlate context enrichment operations with alert tracking for audit trails
  - **Enhanced**: Maintain context usage history linked to alert tracking IDs
  - **Enhanced**: Support context effectiveness measurement per alert correlation
- **BR-CONTEXT-002**: MUST provide context metadata including data freshness, relevance scores, and retrieval costs
- **BR-CONTEXT-003**: MUST support context dependency resolution for complex investigation workflows
- **BR-CONTEXT-004**: MUST enable context type prioritization based on investigation goals
- **BR-CONTEXT-005**: MUST track context usage patterns to optimize future investigations

#### 2.1.2 On-Demand Context Retrieval
- **BR-CONTEXT-006**: MUST fetch **historical Kubernetes cluster intelligence** on-demand based on namespace and resource specifications (organizational metadata, past patterns - NOT current logs/events)
- **BR-CONTEXT-007**: MUST retrieve historical action patterns dynamically using alert type and context signatures
- **BR-CONTEXT-008**: MUST gather **historical metrics trends and patterns** with configurable time windows (trend analysis, anomaly detection - NOT current metric values)
- **BR-CONTEXT-009**: MUST support parallel context fetching to minimize investigation latency
- **BR-CONTEXT-010**: MUST provide context caching with intelligent invalidation strategies

**Clarification**: Context API provides historical intelligence. HolmesGPT toolsets (kubernetes, prometheus) gather current/real-time operational data.

#### 2.1.3 Context Quality Assurance
- **BR-CONTEXT-011**: MUST validate context data freshness and relevance before providing to AI services
- **BR-CONTEXT-012**: MUST filter irrelevant or outdated context to improve signal-to-noise ratio
- **BR-CONTEXT-013**: MUST provide context confidence scores indicating data reliability
- **BR-CONTEXT-014**: MUST detect and handle context data inconsistencies or conflicts
- **BR-CONTEXT-015**: MUST support context data enrichment with calculated insights and patterns

#### 2.1.4 Investigation Complexity Assessment
- **BR-CONTEXT-016**: MUST assess investigation complexity based on alert characteristics (severity, scope, dependencies)
- **BR-CONTEXT-017**: MUST dynamically adjust context gathering strategy based on complexity assessment
- **BR-CONTEXT-018**: MUST classify alerts into complexity tiers (simple, moderate, complex, critical) for context optimization
- **BR-CONTEXT-019**: MUST provide minimum context guarantees for each complexity tier to ensure model effectiveness
- **BR-CONTEXT-020**: MUST escalate to higher context tiers when initial analysis indicates insufficient context

#### 2.1.5 Context Adequacy Validation
- **BR-CONTEXT-021**: MUST validate context adequacy before proceeding with investigation analysis
- **BR-CONTEXT-022**: MUST implement context sufficiency scoring based on investigation requirements
- **BR-CONTEXT-023**: MUST trigger additional context gathering when adequacy scores fall below thresholds
- **BR-CONTEXT-024**: MUST maintain context adequacy metrics per investigation type for continuous improvement
- **BR-CONTEXT-025**: MUST provide context adequacy feedback to AI models for self-assessment capabilities

### 2.2 Performance & Efficiency Requirements

#### 2.2.1 Response Time Targets
- **BR-CONTEXT-026**: MUST provide individual context responses within 100ms for cached data
- **BR-CONTEXT-027**: MUST provide individual context responses within 500ms for fresh data retrieval
- **BR-CONTEXT-028**: MUST support concurrent context requests with linear scalability
- **BR-CONTEXT-029**: MUST maintain 99.9% availability for context API endpoints
- **BR-CONTEXT-030**: MUST provide graceful degradation when context sources are unavailable

#### 2.2.2 Graduated Resource Optimization
- **BR-CONTEXT-031**: MUST implement graduated reduction targets based on investigation complexity rather than fixed percentages
- **BR-CONTEXT-032**: Simple alerts MUST achieve 60-80% payload reduction while maintaining investigation quality
- **BR-CONTEXT-033**: Complex alerts MUST prioritize context completeness over aggressive reduction (20-40% reduction)
- **BR-CONTEXT-034**: Critical alerts MUST ensure maximum context availability with minimal reduction (<20%)
- **BR-CONTEXT-035**: MUST minimize memory footprint through streaming context delivery without compromising model performance
- **BR-CONTEXT-036**: MUST optimize network utilization through intelligent context batching based on investigation needs
- **BR-CONTEXT-037**: MUST provide context compression for large data sets while preserving semantic integrity
- **BR-CONTEXT-038**: MUST implement efficient context deduplication across concurrent investigations

#### 2.2.3 Model Performance Monitoring
- **BR-CONTEXT-039**: MUST monitor AI model performance correlation with context reduction levels
- **BR-CONTEXT-040**: MUST detect when context reduction negatively impacts investigation confidence scores
- **BR-CONTEXT-041**: MUST automatically adjust context gathering when model performance degradation is detected
- **BR-CONTEXT-042**: MUST maintain performance baselines for different context optimization strategies
- **BR-CONTEXT-043**: MUST provide alerting when context reduction causes investigation quality regression

---

## 3. HolmesGPT Toolset Integration

### 3.1 Business Capabilities

#### 3.1.1 Custom Toolset Development
- **BR-HOLMES-001**: MUST provide HolmesGPT with custom Kubernaut toolset for context orchestration
- **BR-HOLMES-002**: MUST enable HolmesGPT to invoke specific context retrieval functions during investigations
- **BR-HOLMES-003**: MUST support toolset function discovery and capability enumeration
- **BR-HOLMES-004**: MUST provide toolset function documentation and usage examples
- **BR-HOLMES-005**: MUST enable toolset function chaining for complex context gathering workflows

#### 3.1.2 Investigation Orchestration
- **BR-HOLMES-006**: MUST allow HolmesGPT to determine context requirements based on alert characteristics
- **BR-HOLMES-007**: MUST enable adaptive context gathering strategies based on investigation progress
- **BR-HOLMES-008**: MUST support conditional context fetching based on intermediate analysis results
- **BR-HOLMES-009**: MUST provide context correlation capabilities for multi-source data integration
- **BR-HOLMES-010**: MUST enable context priority adjustment during active investigations

#### 3.1.3 Fallback & Resilience
- **BR-HOLMES-011**: MUST maintain existing static context enrichment as fallback mechanism
  - **v1**: Static context enrichment when HolmesGPT-API unavailable
  - **v2**: Enhanced fallback with direct LLM integration
- **BR-HOLMES-012**: MUST automatically fallback to static enrichment when dynamic orchestration fails
  - **v1**: Graceful degradation to static context patterns
  - **v2**: Multi-tier fallback (HolmesGPT → Direct LLM → Static)
- **BR-HOLMES-013**: MUST provide investigation quality metrics comparing dynamic vs. static approaches
- **BR-HOLMES-014**: MUST support gradual migration from static to dynamic orchestration
- **BR-HOLMES-015**: MUST preserve all existing investigation capabilities during transition

#### 3.1.4 Dynamic Toolset Configuration
- **BR-HOLMES-016**: MUST dynamically discover available services in Kubernetes cluster to configure HolmesGPT toolsets
- **BR-HOLMES-017**: MUST automatically detect well-known services (Prometheus, Grafana, Jaeger, Elasticsearch) through standard selectors
- **BR-HOLMES-018**: MUST support custom service detection through configurable labels and annotations
- **BR-HOLMES-019**: MUST validate service availability and health before enabling corresponding toolsets
- **BR-HOLMES-020**: MUST provide real-time toolset configuration updates when services are deployed or removed
- **BR-HOLMES-021**: MUST cache service discovery results with intelligent invalidation based on cluster changes
- **BR-HOLMES-022**: MUST generate appropriate toolset configurations with service-specific endpoints and capabilities
- **BR-HOLMES-023**: MUST support toolset configuration templates for common service types (monitoring, logging, tracing)
- **BR-HOLMES-024**: MUST enable toolset priority ordering based on service reliability and investigation relevance
- **BR-HOLMES-025**: MUST provide toolset configuration API endpoints for runtime toolset management
- **BR-HOLMES-026**: MUST implement service discovery health checks with configurable intervals and failure thresholds
- **BR-HOLMES-027**: MUST support multi-namespace service discovery with appropriate RBAC considerations
- **BR-HOLMES-028**: MUST maintain baseline toolsets (Kubernetes, internet) regardless of service discovery results
- **BR-HOLMES-029**: MUST provide service discovery metrics and monitoring for operational visibility
- **BR-HOLMES-030**: MUST support gradual toolset enablement with A/B testing capabilities for new service integrations

#### 3.1.5 Workflow Dependency Specification
- **BR-HOLMES-031**: MUST include step dependencies in remediation recommendations
  - **Requirement**: Each recommendation MUST specify which other recommendations must complete before it can execute
  - **Format**: `dependencies` field containing array of recommendation IDs
  - **Example**: `{"id": "rec-002", "dependencies": ["rec-001"]}` indicates rec-002 depends on rec-001
  - **v1**: HolmesGPT-API response includes dependencies array for each recommendation
  - **Validation**: Dependencies MUST reference valid recommendation IDs within same response
- **BR-HOLMES-032**: MUST specify execution relationships between remediation steps
  - **Relationship Types**: Sequential (explicit dependencies), Parallel (empty dependencies array), Conditional (dependency + condition)
  - **Requirement**: HolmesGPT MUST indicate when steps can execute in parallel (no inter-step dependencies)
  - **Example**: `[{"id": "rec-002", "dependencies": ["rec-001"]}, {"id": "rec-003", "dependencies": ["rec-001"]}]` indicates rec-002 and rec-003 can run in parallel after rec-001
  - **Rationale**: Enable WorkflowExecution Controller to optimize execution through parallel step execution
- **BR-HOLMES-033**: MUST provide dependency graph validation for multi-step workflows
  - **Validation**: Dependency graph MUST be acyclic (no circular dependencies)
  - **Detection**: AIAnalysis service MUST detect circular dependencies before workflow creation
  - **Error Handling**: Reject recommendations with circular dependencies with clear error message
  - **Example Error**: "Circular dependency detected: rec-001 → rec-002 → rec-003 → rec-001"
  - **Fallback**: On validation failure, fall back to sequential execution order

---

## 4. Context API Integration

### 4.1 Business Capabilities

#### 4.1.1 RESTful Context Services
- **BR-API-001**: MUST provide RESTful endpoints for all context types (Kubernetes, metrics, action history)
- **BR-API-002**: MUST support standard HTTP methods with appropriate status codes and error handling
- **BR-API-003**: MUST provide OpenAPI specifications for all context endpoints
- **BR-API-004**: MUST implement proper authentication and authorization for context access
- **BR-API-005**: MUST support content negotiation (JSON, XML, protobuf) based on client preferences

#### 4.1.2 Context Endpoint Specifications
- **BR-API-006**: MUST provide `/api/v1/context/kubernetes/{namespace}/{resource}` for **historical cluster intelligence** (organizational metadata, past resource patterns - NOT current logs/events/status)
- **BR-API-007**: MUST provide `/api/v1/context/metrics/{namespace}/{resource}` for **historical performance trends** (trend analysis, anomaly detection, baseline comparison - NOT current metric values)
- **BR-API-008**: MUST provide `/api/v1/context/action-history/{signalType}` for historical remediation patterns
- **BR-API-009**: MUST provide `/api/v1/context/patterns/{signature}` for pattern matching via PGVector similarity search
- **BR-API-010**: MUST provide `/api/v1/context/health` for service health monitoring

**Clarification**: All endpoints return historical/organizational intelligence from Data Storage Service. HolmesGPT toolsets provide current operational data.

#### 4.1.3 API Quality & Standards
- **BR-API-011**: MUST implement consistent error response formats across all endpoints
- **BR-API-012**: MUST provide comprehensive request validation with descriptive error messages
- **BR-API-013**: MUST support API versioning for backward compatibility
- **BR-API-014**: MUST implement rate limiting and request throttling
- **BR-API-015**: MUST provide comprehensive API documentation with usage examples

---

## 5. Integration Requirements

### 5.1 Internal Integration

#### 5.1.1 AI Service Integration
- **BR-INTEGRATION-001**: MUST integrate seamlessly with existing AIServiceIntegrator patterns
- **BR-INTEGRATION-002**: MUST reuse existing context enrichment logic through API endpoints
- **BR-INTEGRATION-003**: MUST maintain compatibility with LLM fallback mechanisms
  - **v1**: HolmesGPT-API integration with graceful degradation
  - **v2**: Direct LLM integration fallback support
- **BR-INTEGRATION-004**: MUST preserve existing business requirement compliance (BR-AI-011, BR-AI-012, BR-AI-013)
- **BR-INTEGRATION-005**: MUST support hybrid orchestration (both static and dynamic context)
- **BR-CONTEXT-TRACK-001**: MUST integrate with Remediation Processor tracking system
  - Receive alert tracking ID from AI Analysis Engine for context operations
  - Include tracking ID in all HolmesGPT-API context requests and toolset operations
  - Maintain context enrichment audit trail linked to alert tracking
  - Support context quality and relevance metrics per alert correlation

#### 5.1.2 Workflow Engine Integration
- **BR-INTEGRATION-006**: MUST integrate with existing workflow engine orchestration patterns
- **BR-INTEGRATION-007**: MUST support context orchestration within workflow execution pipelines
- **BR-INTEGRATION-008**: MUST provide workflow context checkpoints for complex investigations
- **BR-INTEGRATION-009**: MUST enable workflow-driven context prioritization and optimization
- **BR-INTEGRATION-010**: MUST maintain workflow execution traceability with context decisions

### 5.2 External Integration

#### 5.2.1 HolmesGPT Service Integration
- **BR-EXTERNAL-001**: MUST integrate with HolmesGPT v0.13.1+ custom toolset framework
- **BR-EXTERNAL-002**: MUST support HolmesGPT's async context retrieval patterns
- **BR-EXTERNAL-003**: MUST provide HolmesGPT with standardized context data formats
- **BR-EXTERNAL-004**: MUST enable HolmesGPT to configure context gathering strategies
- **BR-EXTERNAL-005**: MUST support HolmesGPT's investigation state management

#### 5.2.2 Monitoring & Observability Integration
- **BR-EXTERNAL-006**: MUST integrate with existing Prometheus metrics collection
- **BR-EXTERNAL-007**: MUST provide context orchestration metrics to monitoring systems
- **BR-EXTERNAL-008**: MUST support distributed tracing for context gathering workflows
- **BR-EXTERNAL-009**: MUST enable alerting on context orchestration failures
- **BR-EXTERNAL-010**: MUST provide context usage analytics for optimization

---

## 6. Performance Requirements

### 6.1 Quantitative Targets

#### 6.1.1 Response Time Requirements
- **BR-PERF-001**: Context API endpoints MUST respond within 100ms for 95% of cached requests
- **BR-PERF-002**: Context API endpoints MUST respond within 500ms for 95% of fresh data requests
- **BR-PERF-003**: HolmesGPT investigations MUST complete 40-60% faster than static enrichment baseline
- **BR-PERF-004**: Parallel context requests MUST scale linearly up to 50 concurrent investigations
- **BR-PERF-005**: End-to-end investigation latency MUST remain under 10 seconds for 99% of cases

#### 6.1.2 Throughput Requirements
- **BR-PERF-006**: Context API MUST support 1000+ requests per minute during peak investigation periods
- **BR-PERF-007**: System MUST handle 100+ concurrent HolmesGPT investigations without degradation
- **BR-PERF-008**: Context caching MUST achieve 80%+ cache hit rate for repeated investigation patterns
- **BR-PERF-009**: Context gathering MUST support burst scenarios of 500+ requests in 60 seconds
- **BR-PERF-010**: System MUST maintain performance targets during context source failures

#### 6.1.3 Dynamic Toolset Configuration Performance
- **BR-PERF-011**: Service discovery MUST complete within 5 seconds for initial cluster scan
- **BR-PERF-012**: Toolset configuration updates MUST propagate to HolmesGPT within 30 seconds of service changes
- **BR-PERF-013**: Service health checks MUST complete within 2 seconds per service endpoint
- **BR-PERF-014**: Service discovery cache MUST achieve 90%+ hit rate for repeated toolset queries
- **BR-PERF-015**: Dynamic toolset reconfiguration MUST not interrupt ongoing investigations

### 6.2 Resource Utilization

#### 6.2.1 Memory & Network Optimization
- **BR-RESOURCE-001**: Dynamic orchestration MUST reduce investigation memory usage by 50-70%
- **BR-RESOURCE-002**: Total network payload MUST be 60-80% smaller than static enrichment
- **BR-RESOURCE-003**: Context API server MUST use <500MB memory baseline + <50MB per concurrent investigation
- **BR-RESOURCE-004**: Context data streaming MUST minimize client-side memory buffering
- **BR-RESOURCE-005**: System MUST support context garbage collection for completed investigations

#### 6.2.2 CPU & Storage Efficiency
- **BR-RESOURCE-006**: Context orchestration MUST add <10% CPU overhead to existing investigation workflows
- **BR-RESOURCE-007**: Context caching MUST optimize storage utilization through intelligent expiration
- **BR-RESOURCE-008**: Context API MUST support horizontal scaling for increased investigation loads
- **BR-RESOURCE-009**: System MUST provide resource usage metrics for capacity planning
- **BR-RESOURCE-010**: Context gathering MUST minimize storage I/O through efficient data structures

---

## 7. Security Requirements

### 7.1 Access Control & Authentication

#### 7.1.1 Context Data Security
- **BR-SECURITY-001**: Context API MUST authenticate all context requests using existing security mechanisms
- **BR-SECURITY-002**: Context data MUST be filtered based on client authorization levels
- **BR-SECURITY-003**: Sensitive context information MUST be redacted or masked based on security policies
- **BR-SECURITY-004**: Context API MUST implement rate limiting to prevent data enumeration attacks
- **BR-SECURITY-005**: Context requests MUST be logged for security audit trails

#### 7.1.2 HolmesGPT Integration Security
- **BR-SECURITY-006**: HolmesGPT toolset communications MUST use secure transport (TLS 1.2+)
- **BR-SECURITY-007**: Context orchestration MUST validate HolmesGPT request authenticity
- **BR-SECURITY-008**: Context data MUST be encrypted in transit between services
- **BR-SECURITY-009**: Context API MUST implement CORS policies for cross-origin protection
- **BR-SECURITY-010**: System MUST support security policy enforcement for context access patterns

### 7.2 Data Protection & Privacy

#### 7.2.1 Context Data Handling
- **BR-PRIVACY-001**: Context data MUST be handled according to data retention policies
- **BR-PRIVACY-002**: Personal or sensitive information MUST be anonymized in context responses
- **BR-PRIVACY-003**: Context caching MUST respect data privacy and lifecycle requirements
- **BR-PRIVACY-004**: Context API MUST support data deletion requests for compliance
- **BR-PRIVACY-005**: System MUST provide audit trails for all context data access

---

## 8. Reliability Requirements

### 8.1 Availability & Fault Tolerance

#### 8.1.1 Service Reliability
- **BR-RELIABILITY-001**: Context API MUST maintain 99.9% availability during business hours
- **BR-RELIABILITY-002**: System MUST provide graceful degradation when context sources are unavailable
- **BR-RELIABILITY-003**: Context orchestration MUST fallback to static enrichment with <1 second failover
- **BR-RELIABILITY-004**: HolmesGPT integration MUST handle context API failures without investigation termination
- **BR-RELIABILITY-005**: System MUST recover automatically from transient context gathering failures

#### 8.1.2 Error Handling & Recovery
- **BR-RELIABILITY-006**: Context API MUST implement circuit breaker patterns for external data sources
- **BR-RELIABILITY-007**: System MUST provide detailed error diagnostics for context gathering failures
- **BR-RELIABILITY-008**: Context orchestration MUST support retry mechanisms with exponential backoff
- **BR-RELIABILITY-009**: System MUST maintain investigation quality metrics during failure scenarios
- **BR-RELIABILITY-010**: Context gathering MUST support partial success scenarios with degraded context

### 8.2 Data Consistency & Integrity

#### 8.2.1 Context Data Quality
- **BR-CONSISTENCY-001**: Context data MUST be consistent across multiple concurrent requests
- **BR-CONSISTENCY-002**: Context timestamps MUST accurately reflect data collection time
- **BR-CONSISTENCY-003**: Context correlation MUST maintain referential integrity across data sources
- **BR-CONSISTENCY-004**: System MUST detect and handle stale or invalid context data
- **BR-CONSISTENCY-005**: Context caching MUST implement appropriate invalidation strategies

---

## 9. Quality Requirements

### 9.1 Investigation Quality Metrics

#### 9.1.1 Context Relevance & Accuracy
- **BR-QUALITY-001**: Dynamic context gathering MUST achieve 85-95% context relevance scores
- **BR-QUALITY-002**: Context quality MUST be measured through investigation success correlation
- **BR-QUALITY-003**: System MUST provide context quality feedback loops for continuous improvement
- **BR-QUALITY-004**: Context filtering MUST eliminate 90%+ of irrelevant data from investigation scope
- **BR-QUALITY-005**: Context accuracy MUST be validated through automated quality checks

#### 9.1.2 Investigation Outcome Quality
- **BR-QUALITY-006**: Dynamic orchestration MUST maintain or improve investigation confidence scores
- **BR-QUALITY-007**: Context orchestration MUST support investigation reproducibility
- **BR-QUALITY-008**: System MUST provide investigation quality comparisons (dynamic vs. static)
- **BR-QUALITY-009**: Context gathering MUST enable more precise root cause identification
- **BR-QUALITY-010**: Investigation outcomes MUST include context quality assessments

### 9.2 System Quality Attributes

#### 9.2.1 Maintainability & Extensibility
- **BR-MAINTAINABILITY-001**: Context orchestration MUST follow existing code patterns and guidelines
- **BR-MAINTAINABILITY-002**: System MUST support easy addition of new context types and sources
- **BR-MAINTAINABILITY-003**: Context API MUST provide comprehensive testing frameworks
- **BR-MAINTAINABILITY-004**: HolmesGPT toolset MUST be versioned and backward compatible
- **BR-MAINTAINABILITY-005**: System MUST provide clear operational runbooks and troubleshooting guides

---

## 10. Monitoring Requirements

### 10.1 Operational Metrics

#### 10.1.1 Context Orchestration Metrics
- **BR-MONITORING-001**: System MUST track context request volumes and response times per endpoint
- **BR-MONITORING-002**: System MUST monitor context cache hit rates and effectiveness
- **BR-MONITORING-003**: System MUST measure context data freshness and staleness rates
- **BR-MONITORING-004**: System MUST track context orchestration error rates and failure modes
- **BR-MONITORING-005**: System MUST monitor context data size distributions and optimization opportunities

#### 10.1.2 Investigation Quality Metrics
- **BR-MONITORING-006**: System MUST track investigation time improvements from dynamic orchestration
- **BR-MONITORING-007**: System MUST measure context relevance scores for quality assessment
- **BR-MONITORING-008**: System MUST monitor investigation confidence score improvements
- **BR-MONITORING-009**: System MUST track context usage patterns for optimization insights
- **BR-MONITORING-010**: System MUST measure investigation success rates by context gathering strategy
- **BR-MONITORING-016**: System MUST monitor context adequacy scores and their correlation with investigation outcomes
- **BR-MONITORING-017**: System MUST track context reduction impact on model performance across complexity tiers
- **BR-MONITORING-018**: System MUST measure context optimization effectiveness vs. investigation quality trade-offs
- **BR-MONITORING-019**: System MUST alert when context reduction strategies negatively impact business outcomes
- **BR-MONITORING-020**: System MUST provide dashboards showing context optimization balance and model effectiveness

#### 10.1.3 Dynamic Toolset Configuration Metrics
- **BR-MONITORING-011**: System MUST track service discovery success rates and failure modes per service type
- **BR-MONITORING-012**: System MUST monitor toolset configuration update frequency and propagation times
- **BR-MONITORING-013**: System MUST measure service health check success rates and response times
- **BR-MONITORING-014**: System MUST track toolset utilization rates per discovered service
- **BR-MONITORING-015**: System MUST monitor toolset configuration cache hit rates and invalidation patterns

### 10.2 Business Intelligence Metrics

#### 10.2.1 Performance Intelligence
- **BR-BI-001**: System MUST provide analytics on context orchestration efficiency gains
- **BR-BI-002**: System MUST track resource utilization improvements from dynamic context gathering
- **BR-BI-003**: System MUST measure business value delivery through investigation time reduction
- **BR-BI-004**: System MUST provide insights on optimal context gathering strategies
- **BR-BI-005**: System MUST track adoption rates and migration progress from static to dynamic orchestration

---

## 11. Data Requirements

### 11.1 Context Data Management

#### 11.1.1 Data Lifecycle Management
- **BR-DATA-001**: Context data MUST follow defined retention policies based on investigation requirements
- **BR-DATA-002**: Context caching MUST implement intelligent expiration based on data volatility
- **BR-DATA-003**: Historical context patterns MUST be preserved for long-term learning
- **BR-DATA-004**: Context data MUST support versioning for investigation reproducibility
- **BR-DATA-005**: System MUST provide context data archival and retrieval capabilities

#### 11.1.2 Data Quality & Governance
- **BR-DATA-006**: Context data MUST be validated for consistency and completeness
- **BR-DATA-007**: Context metadata MUST include provenance and lineage information
- **BR-DATA-008**: Context data schemas MUST be versioned and backward compatible
- **BR-DATA-009**: System MUST support context data transformation and normalization
- **BR-DATA-010**: Context data MUST comply with organizational data governance policies

---

## 12. Success Criteria

### 12.1 Technical Success Metrics

#### 12.1.1 Performance Achievements
- **SC-TECH-001**: Investigation time reduction of 40-60% compared to static enrichment baseline
- **SC-TECH-002**: Memory utilization reduction of 50-70% for investigation workflows
- **SC-TECH-003**: Network payload reduction of 60-80% through targeted context gathering
- **SC-TECH-004**: Context API response times under 100ms for 95% of cached requests
- **SC-TECH-005**: System availability of 99.9%+ during operational hours

#### 12.1.2 Quality Achievements
- **SC-TECH-006**: Context relevance scores of 85-95% for dynamic orchestration
- **SC-TECH-007**: Investigation confidence scores maintained or improved vs. static enrichment
- **SC-TECH-008**: Context cache hit rates of 80%+ for repeated investigation patterns
- **SC-TECH-009**: Zero regression in existing business requirement compliance
- **SC-TECH-010**: Successful integration with HolmesGPT v0.13.1+ custom toolset framework

#### 12.1.3 Dynamic Toolset Configuration Achievements
- **SC-TECH-011**: Service discovery accuracy of 95%+ for well-known services (Prometheus, Grafana, Jaeger)
- **SC-TECH-012**: Toolset configuration update propagation within 30 seconds of service changes
- **SC-TECH-013**: Service health check success rate of 98%+ with 2-second response time targets
- **SC-TECH-014**: Zero-configuration deployment achieving automatic toolset enablement
- **SC-TECH-015**: Support for 10+ concurrent service discovery operations without performance degradation

### 12.2 Business Success Metrics

#### 12.2.1 Operational Excellence
- **SC-BUSINESS-001**: 90%+ user satisfaction with investigation speed and accuracy
- **SC-BUSINESS-002**: Seamless migration from static to dynamic orchestration with zero downtime
- **SC-BUSINESS-003**: Enhanced investigation capabilities enabling more complex troubleshooting scenarios
- **SC-BUSINESS-004**: Demonstrated ROI through reduced investigation time and improved accuracy
- **SC-BUSINESS-005**: Successful production deployment with full monitoring and observability

#### 12.2.2 Strategic Value
- **SC-STRATEGIC-001**: Platform foundation established for AI-driven investigation orchestration
- **SC-STRATEGIC-002**: Architecture scalability validated for future AI service integrations
- **SC-STRATEGIC-003**: Context orchestration framework enabling rapid addition of new context sources
- **SC-STRATEGIC-004**: Development guidelines compliance maintaining code quality and patterns
- **SC-STRATEGIC-005**: Knowledge transfer and documentation enabling team independence

---

## 13. Risk Management

### 13.1 Technical Risks

#### 13.1.1 Implementation Risks
- **RISK-TECH-001**: **Network Latency Impact** - Dynamic context fetching may increase investigation latency
  - *Mitigation*: Implement parallel context fetching and intelligent caching strategies
- **RISK-TECH-002**: **Context API Dependency** - Single point of failure for investigation workflows
  - *Mitigation*: Maintain static enrichment fallback and implement circuit breaker patterns
- **RISK-TECH-003**: **HolmesGPT Integration Complexity** - Custom toolset development may be complex
  - *Mitigation*: Start with proof-of-concept and follow HolmesGPT best practices
- **RISK-TECH-004**: **Over-Aggressive Context Reduction** - Fixed reduction targets may compromise model effectiveness
  - *Mitigation*: Implement graduated reduction based on complexity assessment and continuous performance monitoring
- **RISK-TECH-005**: **Context Inadequacy** - Insufficient context may lead to poor investigation outcomes
  - *Mitigation*: Implement context adequacy validation and automatic escalation to higher context tiers

#### 13.1.2 Performance Risks
- **RISK-PERF-001**: **Context Cache Invalidation** - Poor cache strategies may degrade performance
  - *Mitigation*: Implement intelligent cache policies and monitoring
- **RISK-PERF-002**: **Concurrent Investigation Load** - High concurrency may overwhelm context services
  - *Mitigation*: Design for horizontal scaling and implement rate limiting

### 13.2 Business Risks

#### 13.2.1 Operational Risks
- **RISK-BUSINESS-001**: **Migration Complexity** - Transition from static to dynamic may disrupt operations
  - *Mitigation*: Implement gradual migration with A/B testing capabilities
- **RISK-BUSINESS-002**: **User Acceptance** - Teams may resist change from proven static enrichment
  - *Mitigation*: Demonstrate clear performance benefits and maintain fallback options

---

## 14. Acceptance Criteria

### 14.1 Functional Acceptance

#### 14.1.1 Core Functionality
- [ ] Context API endpoints operational and responding within performance targets
- [ ] HolmesGPT custom toolset successfully integrated and functional
- [ ] Dynamic context orchestration achieving target performance improvements
- [ ] Fallback mechanisms working seamlessly when dynamic orchestration fails
- [ ] All existing business requirements (BR-AI-011, BR-AI-012, BR-AI-013) maintained
- [ ] Dynamic toolset configuration automatically detecting and configuring services
- [ ] Service discovery accurately identifying well-known services (Prometheus, Grafana, Jaeger)
- [ ] Toolset configuration updates propagating in real-time upon service changes
- [ ] Investigation complexity assessment correctly categorizing alerts into complexity tiers
- [ ] Context adequacy validation preventing insufficient context scenarios
- [ ] Graduated reduction targets maintaining model effectiveness across all complexity levels
- [ ] Model performance monitoring detecting and preventing context reduction impact

#### 14.1.2 Integration Validation
- [ ] End-to-end investigation workflows using dynamic context orchestration
- [ ] Monitoring and alerting systems tracking context orchestration metrics
- [ ] Security and authentication mechanisms validated for context API access
- [ ] Performance benchmarks demonstrating quantified improvements
- [ ] Documentation and operational runbooks completed and validated

### 14.2 Business Acceptance

#### 14.2.1 Value Delivery
- [ ] Investigation time reduction targets achieved and measured
- [ ] Resource utilization improvements demonstrated and quantified
- [ ] Investigation quality maintained or improved vs. baseline
- [ ] Team productivity improvements through enhanced investigation capabilities
- [ ] Strategic platform foundation established for future AI service integrations

---

## 9. Context Optimization (V1 Enhancement)

### 9.1 Priority-Based Context Selection

#### **BR-CONTEXT-OPT-V1-001: Priority-Based Context Selection**
**Business Requirement**: The system MUST implement intelligent priority-based context selection that optimizes context gathering based on investigation requirements and business priorities.

**Functional Requirements**:
1. **Priority Scoring** - MUST implement priority scoring algorithms for context data based on relevance and business impact
2. **Intelligent Selection** - MUST intelligently select the most relevant context data for each investigation
3. **Resource Optimization** - MUST optimize resource usage by prioritizing high-value context data
4. **Dynamic Prioritization** - MUST dynamically adjust priorities based on investigation progress and findings

**Success Criteria**:
- 85% accuracy in priority-based context selection
- 30% reduction in context gathering time through intelligent prioritization
- 90% relevance score for selected context data
- Dynamic prioritization with real-time adjustment capabilities

**Business Value**: Optimized context gathering improves investigation efficiency and reduces resource overhead

#### **BR-CONTEXT-OPT-V1-002: Single-Tier Context Management**
**Business Requirement**: The system MUST provide optimized single-tier context management specifically designed for HolmesGPT-API integration with streamlined data flow and minimal overhead.

**Functional Requirements**:
1. **Streamlined Data Flow** - MUST implement streamlined data flow optimized for single AI provider scenarios
2. **Minimal Overhead** - MUST minimize context management overhead for HolmesGPT-API integration
3. **Optimized Caching** - MUST implement optimized caching strategies for single-tier context management
4. **Performance Optimization** - MUST optimize performance for HolmesGPT-API specific context requirements

**Success Criteria**:
- <500ms context retrieval time for 95% of requests
- 40% reduction in context management overhead compared to multi-tier approaches
- 90% cache hit rate for frequently accessed context data
- Optimized performance specifically for HolmesGPT-API integration patterns

**Business Value**: Streamlined context management enhances HolmesGPT-API performance and reduces operational complexity

#### **BR-CONTEXT-OPT-V1-003: Context Quality Assurance**
**Business Requirement**: The system MUST implement comprehensive context quality assurance mechanisms to ensure context data meets investigation standards and reliability requirements.

**Functional Requirements**:
1. **Quality Scoring** - MUST implement quality scoring algorithms for context data
2. **Data Validation** - MUST validate context data accuracy and completeness
3. **Quality Monitoring** - MUST continuously monitor context quality metrics
4. **Quality Improvement** - MUST implement quality improvement mechanisms based on feedback

**Success Criteria**:
- 90% quality score accuracy for context data
- 95% data validation success rate
- Real-time quality monitoring with <1 minute update frequency
- Continuous quality improvement with measurable metrics

**Business Value**: High-quality context data improves investigation accuracy and reliability

---

---

## 15. Architectural Clarification & Updates (January 2025)

### 15.1 Context API vs. HolmesGPT Toolsets - Clear Separation

**Architectural Update**: Following HolmesGPT integration architecture design, Context API role has been clarified to focus exclusively on historical and organizational intelligence, complementing (not duplicating) HolmesGPT's real-time data gathering capabilities.

### 15.2 Dual-Source Intelligence Gathering Model

| Data Source | Purpose | Answers | Data Examples | Technology |
|-------------|---------|---------|---------------|------------|
| **HolmesGPT Toolsets** | Real-time operational data | "What's happening NOW?" | Current pod logs, live CPU/memory metrics, current K8s events, kubectl describe output | HolmesGPT SDK (kubernetes, prometheus toolsets) |
| **Context API Service** | Historical & organizational intelligence | "What happened BEFORE?" + "What's the organizational context?" | Past remediation actions, historical metric trends, similar alert patterns, business priority, SLA requirements | PostgreSQL + PGVector |

### 15.3 Updated Business Requirement Interpretations

**Clarified Requirements**:
- **BR-CONTEXT-006**: "Kubernetes cluster context" = Historical cluster intelligence (NOT current logs/events)
- **BR-CONTEXT-008**: "Real-time metrics context" = Historical trends delivered in real-time (NOT current Prometheus scrapes)
- **BR-API-006**: Cluster context endpoint = Organizational metadata, past patterns
- **BR-API-007**: Performance metrics endpoint = Historical trends, anomaly detection (NOT current values)

**Unchanged Requirements** (already accurate):
- **BR-CONTEXT-007**: Historical action patterns ✅
- **BR-API-008**: Action history ✅
- **BR-API-009**: Pattern matching ✅

### 15.4 Data Source Clarification

**Context API Data Sources**:
- ✅ **Primary**: Data Storage Service (PostgreSQL for historical data, PGVector for similarity search)
- ❌ **NOT**: Direct Kubernetes API access (HolmesGPT kubernetes toolset handles this)
- ❌ **NOT**: Direct Prometheus queries (HolmesGPT prometheus toolset handles this)

**HolmesGPT Toolsets Data Sources**:
- ✅ **kubernetes toolset**: Direct Kubernetes API access (logs, events, describe)
- ✅ **prometheus toolset**: Direct Prometheus API access (current metrics)
- ✅ **grafana toolset**: Direct Grafana API access (dashboards)

### 15.5 Integration Pattern

**Investigation Flow**:
```
1. AIAnalysis Service → HolmesGPT API (investigation request)
2. HolmesGPT API → Context API (fetch historical intelligence)
   Returns: Action history, pattern analysis, organizational context, metric trends
3. HolmesGPT API → HolmesGPT SDK (investigation with toolsets)
   - kubernetes toolset: Fetches current logs, events, resource status
   - prometheus toolset: Queries current metrics
   - Combined with historical context from step 2
4. HolmesGPT API → AIAnalysis Service (investigation results)
   Returns: Comprehensive analysis (real-time data + historical intelligence)
```

### 15.6 Performance Impact

**Clarified Performance Benefits**:
- **40-60% investigation improvement**: From targeted historical context (not full database dumps)
- **50-70% payload reduction**: Compressed historical trends vs. raw time-series data
- **15-25% accuracy improvement**: Historical patterns guide investigation strategy

**Note**: HolmesGPT toolsets fetch real-time data directly (not through Context API), ensuring fresh operational data.

### 15.7 Migration Impact

**No Breaking Changes**: This is a clarification of existing architecture, not a change:
- Context API was always intended to provide historical intelligence
- HolmesGPT toolsets were always intended to fetch real-time data
- Original BR requirements used ambiguous language ("real-time metrics context") which has now been clarified

**Updated Documents**:
- ✅ `10_AI_CONTEXT_ORCHESTRATION.md` (this document) - BR-CONTEXT-006, BR-CONTEXT-008, BR-API-006, BR-API-007 clarified
- ✅ `08-kubernaut-agent.md` (stateless service spec) - Already reflects correct architecture
- ✅ `REMAINING_SERVICES_PLAN.md` (service planning) - Context API description updated

---

**Document Approval**:
- Technical Architect: _________________ Date: _______
- Product Owner: _________________ Date: _______
- Engineering Manager: _________________ Date: _______

**Document Version**: 1.1 (Updated January 2025 - Architectural Clarification)
**Previous Version**: 1.0 (January 2025 - Original)
