# AI & Machine Learning Components - Business Requirements

**Document Version**: 1.1
**Date**: November 2025
**Status**: Business Requirements Specification
**Module**: AI & Machine Learning (`pkg/ai/`)

---

## Changelog

| Version | Date | Changes |
|---------|------|---------|
| 1.1 | 2025-11-30 | Added BR-AI-075-076 (Workflow Selection), BR-AI-080-083 (Recovery Flow) |
| 1.0 | 2025-01-15 | Initial version |

---

## 1. Purpose & Scope

### 1.1 Business Purpose
The AI & Machine Learning components provide intelligent decision-making capabilities for Kubernetes remediation, leveraging multiple LLM providers, historical learning, and advanced analytics to deliver autonomous, context-aware remediation actions.

### 1.2 Scope
- **AI Common Layer**: Core AI service interfaces and abstractions
- **AI Conditions Engine**: LLM-powered condition evaluation and optimization
- **AI Insights Service**: Effectiveness assessment and continuous learning
- **LLM Integration Layer**: Multi-provider LLM client management

---

## 2. AI Common Layer

### 2.1 Business Capabilities

#### 2.1.1 Analysis Provider
- **BR-AI-001**: MUST provide contextual analysis of Kubernetes alerts and system state
  - **Enhanced**: Receive alert tracking ID from Remediation Processor for analysis correlation
  - **Enhanced**: Include alert tracking ID in all analysis results and recommendations
  - **Enhanced**: Maintain analysis history linked to specific alert tracking IDs
  - **Enhanced**: Support analysis effectiveness tracking per alert correlation
- **BR-AI-002**: MUST support multiple analysis types (diagnostic, predictive, prescriptive)
- **BR-AI-003**: MUST generate structured analysis results with confidence scoring
- **BR-AI-004**: MUST correlate analysis results across multiple data sources
- **BR-AI-005**: MUST maintain analysis history for trend identification

#### 2.1.2 Recommendation Provider
- **BR-AI-006**: MUST generate actionable remediation recommendations based on alert context
- **BR-AI-007**: MUST rank recommendations by effectiveness probability
- **BR-AI-008**: MUST consider historical success rates in recommendation scoring
- **BR-AI-009**: MUST support constraint-based recommendation filtering
- **BR-AI-010**: MUST provide recommendation explanations with supporting evidence

#### 2.1.3 Investigation Provider
- **BR-AI-011**: MUST conduct intelligent alert investigation using historical patterns
  - **Enhanced**: Use alert tracking ID to correlate with historical investigation patterns
  - **Enhanced**: Include tracking ID in HolmesGPT-API investigation requests (v1)
  - **Enhanced**: Maintain investigation results linked to alert tracking for pattern learning
  - **Enhanced**: Support investigation quality metrics per alert tracking correlation
  - **v1**: HolmesGPT-API integration (primary implementation)
  - **v2**: Multi-provider support with LLM fallback mechanisms
- **BR-AI-012**: MUST identify root cause candidates with supporting evidence
  - **v1**: HolmesGPT-API investigation capabilities
  - **v2**: Enhanced with direct LLM integration fallback
- **BR-AI-013**: MUST correlate alerts across time windows and resource boundaries
  - **v1**: HolmesGPT-API correlation features
  - **v2**: Multi-provider correlation with intelligent routing
- **BR-AI-014**: MUST generate investigation reports with actionable insights
  - **v1**: HolmesGPT-API structured investigation results
- **BR-AI-015**: MUST support custom investigation scopes and time windows
  - **v1**: HolmesGPT-API custom toolset configurations

### 2.2 Service Health & Monitoring
- **BR-AI-016**: MUST provide real-time health status for all AI services
- **BR-AI-017**: MUST track service performance metrics (latency, throughput, error rates)
- **BR-AI-018**: MUST implement dependency health monitoring
- **BR-AI-019**: MUST support service degradation detection and alerting
- **BR-AI-020**: MUST maintain service availability above 99.5% SLA

### 2.3 Quality Assurance
- **BR-AI-021**: MUST validate AI responses for completeness and accuracy
- **BR-AI-022**: MUST implement confidence thresholds for automated decision making
- **BR-AI-023**: MUST detect and handle AI hallucinations or invalid responses
- **BR-AI-024**: MUST provide fallback mechanisms when AI services are unavailable
  - **v1**: Graceful degradation with error handling (HolmesGPT-API unavailable)
  - **v2**: Direct LLM integration fallback with context enrichment
- **BR-AI-025**: MUST maintain response quality metrics and improvement tracking
- **BR-AI-051**: MUST validate AI responses for dependency completeness and correctness
  - **Validation Checks**: All recommendation dependencies MUST reference valid recommendation IDs within the same response
  - **Completeness**: No missing dependency references (all referenced IDs exist in recommendations list)
  - **Correctness**: Dependency IDs MUST be unique and correctly formatted
  - **Error Handling**: Reject response with clear error if dependencies reference non-existent recommendations
  - **v1**: AIAnalysis service validates HolmesGPT response dependencies
- **BR-AI-052**: MUST detect circular dependencies in AI recommendation graphs
  - **Detection Algorithm**: Implement topological sort or cycle detection algorithm
  - **Validation Timing**: Perform circular dependency detection before WorkflowExecution CRD creation
  - **Error Response**: Return specific error identifying circular dependency chain (e.g., "rec-001 → rec-002 → rec-003 → rec-001")
  - **Fallback Strategy**: On circular dependency detection, fall back to sequential execution order
  - **v1**: AIAnalysis service implements dependency graph cycle detection
- **BR-AI-053**: MUST handle missing or invalid dependencies with intelligent fallback
  - **Missing Dependencies**: If dependencies field missing from recommendation, default to empty array (no dependencies)
  - **Invalid Dependencies**: If dependency references invalid ID, log warning and remove invalid reference
  - **Fallback Behavior**: Default to sequential execution if dependency validation fails
  - **Notification**: Notify via Notification Service when dependency validation fails
  - **Audit Trail**: Log all dependency validation failures with complete context for investigation

### 2.4 Approval & Policy Management
- **BR-AI-026**: MUST evaluate AI recommendations against configurable approval policies before execution
  - **Implementation**: OPA/Rego policy engine for approval decisions
  - **Policy Storage**: ConfigMap `ai-approval-policies` in `kubernaut-system` namespace
  - **Evaluation**: Policy evaluation before creating AIApprovalRequest CRD
- **BR-AI-027**: MUST secure approval actions with Kubernetes RBAC
  - **Implementation**: Separate `AIApprovalRequest` CRD with role-based access control
  - **RBAC**: Tiered approval permissions (Junior SRE, SRE, Senior SRE, Platform Admin)
  - **Audit**: Complete audit trail for all approval decisions
- **BR-AI-028**: MUST implement Rego-based approval policies for flexible policy management
  - **Policy Structure**: Environment-specific packages (production, development, common helpers)
  - **Testing**: All policies MUST have unit tests (`opa test`)
  - **Validation**: Policies MUST pass `opa check` before deployment
- **BR-AI-029**: MUST support zero-downtime policy updates
  - **Mechanism**: Update ConfigMap, controller watches for changes, reloads policies
  - **Rollback**: Keep previous ConfigMap version for rollback capability
  - **Validation**: Policy syntax validation before applying updates
- **BR-AI-030**: MUST maintain policy audit trail for all approval decisions
  - **Storage**: AIApprovalRequest.status.policyEvaluation contains policy name and matched rules
  - **Query**: `kubectl get aiapprovalrequest -o jsonpath='{.status.policyEvaluation}'`
  - **Metrics**: Track approval decisions by policy name, environment, and action type

### 2.5 Data Management & Storage
- **BR-AI-031**: MUST handle large enriched payloads without exceeding Kubernetes etcd limits
  - **Size Thresholds**: 50KB per log entry, 100KB total embedded data
  - **Strategy**: Selective embedding - small data embedded in CRD, large data stored externally
  - **External Storage**: PostgreSQL table `aianalysis_large_payloads` with references in CRD
  - **Compression**: Support gzip compression for stored data (not embedded)
- **BR-AI-032**: MUST implement phase-specific timeouts for AI analysis workflows
  - **Default Timeouts**: investigating (15min), analyzing (10min), recommending (5min)
  - **Configuration**: Configurable via annotations (`ai.kubernaut.ai/<phase>-timeout`)
  - **Detection**: Automatic phase timeout detection with failure handling
  - **Metrics**: Track timeout occurrences by phase and environment
- **BR-AI-033**: MUST gracefully handle missing historical success rate data
  - **Tier 1 (Primary)**: Direct action match from historical data (≥5 samples)
  - **Tier 2 (Fallback)**: Action category match (resource-scaling, pod-restart, etc.)
  - **Tier 3 (Fallback)**: Environment-specific baseline (production: 0.75, dev: 0.85)
  - **Tier 4 (Final)**: Global baseline (0.70) with low confidence indicator
  - **Confidence Tracking**: Include fallback tier and confidence level in recommendations

### 2.6 Pattern Analysis & Historical Context
- **BR-AI-037**: MUST analyze historical patterns before recommending resource increases
  - **Frequency Analysis**: Analyze event frequency over 1h, 24h, 7d windows
  - **Trend Analysis**: Calculate resource usage trends and growth rates
  - **Anomaly Detection**: Distinguish transient spikes from chronic patterns
  - **Leak Detection**: Detect memory leaks via heap growth rate + GC pressure analysis
  - **Evidence Requirement**: Single events MUST NOT trigger declarative changes (e.g., Git PRs)
  - **Pattern Threshold**: Repeated events (3+ in 1h) with supporting evidence MAY trigger declarative changes
  - **Justification**: Generate evidence-based justification for all resource increase recommendations
  - **Integration**: Works with BR-OSC-* (oscillation detection) for comprehensive stability
  - **Confidence Tracking**: Include pattern analysis confidence in all recommendations

### 2.7 Workflow Selection & Output Contract

> **Added**: v1.1 (2025-11-30) - Formalizes workflow selection output requirements per DD-CONTRACT-001

- **BR-AI-075**: MUST produce structured workflow selection output per ADR-041 LLM contract
  - **Output Fields**: `workflowId` (UUID from catalog), `containerImage` (OCI reference), `parameters` (map)
  - **Catalog Integration**: Selected workflow MUST exist in Data Storage workflow catalog
  - **Container Resolution**: HolmesGPT-API resolves `workflowId` → `containerImage` during MCP search
  - **Parameter Validation**: Parameters MUST conform to workflow's parameter schema
  - **Reference**: DD-CONTRACT-001 v1.2, DD-WORKFLOW-002 v3.3
- **BR-AI-076**: MUST provide rich approval context when confidence is below threshold (<80%)
  - **Approval Context Fields**: `investigationSummary`, `rootCauseEvidence`, `recommendationRationale`, `riskAssessment`
  - **Threshold**: Auto-approval only when confidence ≥80%, otherwise `approvalRequired=true`
  - **V1.0 Flow**: AIAnalysis sets flag, Remediation Orchestrator triggers notification
  - **V1.1 Flow**: Creates `RemediationApprovalRequest` CRD for approval workflow
  - **Reference**: ADR-040, ADR-018

### 2.8 Recovery Flow

> **Added**: v1.1 (2025-11-30) - Formalizes recovery attempt handling per DD-RECOVERY-002

- **BR-AI-080**: MUST support recovery analysis for failed WorkflowExecution attempts
  - **Trigger**: Remediation Orchestrator creates new AIAnalysis CRD with `isRecoveryAttempt=true`
  - **Attempt Tracking**: `recoveryAttemptNumber` increments for each retry (1, 2, 3, ...)
  - **Max Attempts**: Configurable via annotation (default: 3)
  - **Reference**: DD-RECOVERY-002
- **BR-AI-081**: MUST accept and utilize previous execution context for recovery analysis
  - **Input**: `previousExecutions` array containing ALL prior attempt contexts
  - **Context Fields**: `workflowId`, `containerImage`, `failureReason`, `failurePhase`, `kubernetesReason`
  - **Purpose**: Enable LLM to learn from failures and avoid repeating mistakes
  - **Reference**: DD-RECOVERY-003
- **BR-AI-082**: MUST call HolmesGPT-API recovery endpoint for failed workflow analysis
  - **Endpoint**: `POST /api/v1/recovery/analyze`
  - **Payload**: Original context + failure context + Kubernetes reason codes
  - **Response**: New workflow recommendation avoiding previous failure causes
  - **Reference**: DD-RECOVERY-003, BR-HAPI-RECOVERY-001
- **BR-AI-083**: MUST reuse original enrichment data without re-enriching for recovery attempts
  - **Source**: `spec.enrichmentResults` copied from original SignalProcessing CRD
  - **Rationale**: Signal context hasn't changed; re-enriching wastes resources
  - **Optimization**: Skip SignalProcessing reconciliation for recovery AIAnalysis CRDs
  - **Reference**: DD-RECOVERY-002

---

## 3. AI Conditions Engine

### 3.1 Business Capabilities

#### 3.1.1 Condition Evaluation
- **BR-COND-001**: MUST evaluate complex logical conditions using natural language processing
- **BR-COND-002**: MUST support dynamic condition parsing from alert content
- **BR-COND-003**: MUST handle temporal conditions with time-based evaluation
- **BR-COND-004**: MUST evaluate conditions across multiple Kubernetes resources
- **BR-COND-005**: MUST provide condition evaluation confidence scoring

#### 3.1.2 Learning Integration
- **BR-COND-006**: MUST learn from condition evaluation outcomes to improve accuracy
- **BR-COND-007**: MUST adapt condition evaluation based on environmental patterns
- **BR-COND-008**: MUST maintain condition evaluation history for analysis
- **BR-COND-009**: MUST identify frequently occurring condition patterns
- **BR-COND-010**: MUST optimize condition evaluation performance through learning

#### 3.1.3 Performance Metrics
- **BR-COND-011**: MUST track condition evaluation accuracy rates
- **BR-COND-012**: MUST measure condition evaluation latency and optimize performance
- **BR-COND-013**: MUST monitor condition complexity and processing resource usage
- **BR-COND-014**: MUST provide condition evaluation success/failure analytics
- **BR-COND-015**: MUST implement condition evaluation performance baselines

#### 3.1.4 Prompt Optimization
- **BR-COND-016**: MUST continuously optimize prompts for improved condition evaluation
- **BR-COND-017**: MUST adapt prompts based on LLM provider capabilities
- **BR-COND-018**: MUST maintain prompt versioning for performance comparison
- **BR-COND-019**: MUST implement A/B testing for prompt optimization
- **BR-COND-020**: MUST provide prompt effectiveness analytics and insights

---

## 4. AI Insights Service (Effectiveness Monitor)

**Architecture**: **Hybrid Approach** (Automated Checks + Selective AI Analysis)
- **Effectiveness Monitor Service**: GO service performing automated checks, metrics collection, and data aggregation
- **HolmesGPT API Integration**: AI-powered pattern analysis, root cause validation, and lesson extraction (selective)
- **Design Decision**: [DD-EFFECTIVENESS-001: Hybrid Automated + AI Analysis Approach](../architecture/decisions/)

### 4.1 Business Capabilities

#### 4.1.1 Effectiveness Assessment

##### **Automated Assessment (Effectiveness Monitor - Always Executed)**
- **BR-INS-001**: MUST assess the effectiveness of executed remediation actions using automated checks
  - **Implementation**: Effectiveness Monitor service (GO)
  - **Scope**: Technical validation, metric comparison, health checks
  - **Examples**: Pod running status, OOM error count, latency metrics, readiness probes
  - **Frequency**: Every workflow execution
  - **Dependencies**: BR-EXEC-027 to BR-EXEC-030 (action outcome verification)

##### **AI-Powered Analysis (HolmesGPT API - Selective Execution)**
- **BR-INS-002**: MUST correlate action outcomes with environmental improvements using AI analysis
  - **Implementation**: HolmesGPT API `/api/v1/postexec/analyze` endpoint
  - **Scope**: Causation analysis (not just correlation), root cause validation, unintended consequences
  - **Triggers**: P0 failures, new action types, suspected oscillations, periodic batch analysis
  - **Rationale**: AI distinguishes "problem masked" from "problem solved" (see DD-EFFECTIVENESS-001)

- **BR-INS-003**: MUST track long-term effectiveness trends for different action types
  - **Implementation**: Data Storage (automated metrics) + HolmesGPT API (pattern analysis)
  - **Automated**: Time-series metrics, success rates, execution times
  - **AI Analysis**: Context-aware patterns (e.g., "gradual scaling better for Java memory leaks")

- **BR-INS-004**: MUST identify actions that consistently produce positive outcomes
  - **Implementation**: Effectiveness Monitor (data aggregation) + HolmesGPT API (pattern recognition)
  - **Automated**: Success rate calculations, metric improvements
  - **AI Analysis**: Context-specific effectiveness (environment, workload type, time of day)

- **BR-INS-005**: MUST detect actions that cause adverse effects or oscillations
  - **Implementation**: Effectiveness Monitor (metric anomaly detection) + HolmesGPT API (causation analysis)
  - **Automated**: Detect metric changes (CPU throttling after memory increase)
  - **AI Analysis**: Explain causation ("Fix OOM → JVM heap expansion → GC pauses → CPU throttling")
  - **Critical**: Prevents remediation loops (BR-WF-541)

#### 4.1.2 Analytics Engine (Pattern Learning - AI-Powered)
- **BR-INS-006**: MUST provide advanced pattern recognition across remediation history
  - **Implementation**: HolmesGPT API with historical data from Context API
  - **Scope**: Cross-remediation patterns, context-aware insights, trend analysis

- **BR-INS-007**: MUST generate insights on optimal remediation strategies
  - **Implementation**: HolmesGPT API analyzing effectiveness data across contexts
  - **Examples**: "Gradual scaling 95% effective vs immediate scaling 80% effective for stateful apps"

- **BR-INS-008**: MUST identify seasonal or temporal patterns in system behavior
  - **Implementation**: HolmesGPT API analyzing time-series effectiveness data
  - **Examples**: "OOM incidents increase 3x during peak traffic hours"

- **BR-INS-009**: MUST detect emerging issues before they become critical alerts
  - **Implementation**: HolmesGPT API analyzing effectiveness degradation trends
  - **Examples**: "Action effectiveness declining 5% week-over-week"

- **BR-INS-010**: MUST provide predictive insights for capacity planning
  - **Implementation**: HolmesGPT API analyzing resource utilization trends
  - **Examples**: "Current growth rate will exhaust capacity in 45 days"

#### 4.1.3 Continuous Learning
- **BR-INS-011**: MUST continuously improve decision-making based on outcomes
- **BR-INS-012**: MUST adapt to changing environmental conditions and requirements
- **BR-INS-013**: MUST learn from both successful and failed remediation attempts
- **BR-INS-014**: MUST maintain learning model accuracy through validation
- **BR-INS-015**: MUST implement feedback loops for human operator corrections

#### 4.1.4 Reporting & Visualization
- **BR-INS-016**: MUST generate comprehensive effectiveness reports
- **BR-INS-017**: MUST provide trend analysis and forecasting capabilities
- **BR-INS-018**: MUST create actionable insights for operational improvement
- **BR-INS-019**: MUST support customizable reporting timeframes and metrics
- **BR-INS-020**: MUST export insights data for integration with external tools

---

## 5. LLM Integration Layer

### 5.1 Business Capabilities

#### 5.1.1 Multi-Provider Support
- **BR-LLM-001**: MUST support OpenAI GPT models (GPT-3.5, GPT-4, GPT-4o)
  - **v1**: HolmesGPT-API handles LLM provider integration
  - **v2**: Direct AI Analysis Engine integration for fallback
- **BR-LLM-002**: MUST support Anthropic Claude models (Claude-3, Claude-3.5)
  - **v1**: HolmesGPT-API handles provider integration
  - **v2**: Direct AI Analysis Engine integration for fallback
- **BR-LLM-003**: MUST support Azure OpenAI Service integration
  - **v1**: HolmesGPT-API handles provider integration
  - **v2**: Direct AI Analysis Engine integration for fallback
- **BR-LLM-004**: MUST support AWS Bedrock model access
  - **v1**: HolmesGPT-API handles provider integration
  - **v2**: Direct AI Analysis Engine integration for fallback
- **BR-LLM-005**: MUST support local model inference (Ollama, Ramalama)
  - **v1**: HolmesGPT-API handles local model integration
  - **v2**: Direct AI Analysis Engine integration for cost optimization

#### 5.1.2 Enhanced Client Capabilities
- **BR-LLM-006**: MUST provide intelligent response parsing with error handling
- **BR-LLM-007**: MUST implement response validation and quality scoring
- **BR-LLM-008**: MUST support streaming responses for large outputs
- **BR-LLM-009**: MUST handle rate limiting and quota management across providers
- **BR-LLM-010**: MUST implement cost optimization strategies for API usage
  - **v1**: HolmesGPT-API handles cost optimization internally
  - **v2**: AI Analysis Engine implements intelligent model selection and cost optimization

#### 5.1.3 Response Processing
- **BR-LLM-011**: MUST parse structured JSON responses with defined schema validation
- **BR-LLM-012**: MUST validate response format and content completeness
- **BR-LLM-013**: MUST extract actionable items from structured JSON responses
- **BR-LLM-014**: MUST handle partial or malformed JSON responses with intelligent fallback
- **BR-LLM-015**: MUST implement response caching for repeated queries

#### 5.1.4 Prompt Engineering
- **BR-LLM-016**: MUST optimize prompts for different model capabilities and contexts
- **BR-LLM-017**: MUST support dynamic prompt generation based on alert content
- **BR-LLM-018**: MUST implement prompt templates for consistent outputs
- **BR-LLM-019**: MUST provide prompt version control and A/B testing
- **BR-LLM-020**: MUST maintain prompt performance analytics
- **BR-LLM-035**: MUST instruct LLM to generate step dependencies in remediation recommendations
  - **Prompt Requirement**: Include explicit instructions for LLM to specify dependencies between remediation steps
  - **Format Specification**: Prompt MUST request `dependencies` array field for each recommendation
  - **Guidance**: Provide examples in prompt showing proper dependency specification
  - **Example Instruction**: "For each recommendation, specify 'dependencies': array of recommendation IDs that must complete first"
  - **v1**: HolmesGPT prompt engineering for dependency specification
- **BR-LLM-036**: MUST request execution order specification in prompts
  - **Ordering Guidance**: Prompt MUST instruct LLM to consider execution order and parallelization opportunities
  - **Parallel Detection**: Prompt MUST guide LLM to identify steps that can execute in parallel
  - **Example Instruction**: "If multiple steps can execute simultaneously (no dependencies between them), specify empty dependencies array"
  - **Sequential Guidance**: "If step B requires step A to complete, specify: dependencies: ['step-A-id']"
- **BR-LLM-037**: MUST define response schema with dependencies field in prompts
  - **Schema Definition**: Prompt MUST include complete JSON schema with dependencies field
  - **Required Fields**: id, action, parameters, dependencies (array), confidence, reasoning
  - **Example Schema**: `{"id": "string", "action": "string", "parameters": {}, "dependencies": ["string"], "confidence": 0.0-1.0, "reasoning": "string"}`
  - **Validation**: Schema MUST be validated against response before acceptance

#### 5.1.5 Structured Response Generation
- **BR-LLM-021**: MUST enforce JSON-structured responses from LLM providers for machine actionability
- **BR-LLM-022**: MUST validate JSON response schema compliance and completeness
- **BR-LLM-023**: MUST handle malformed JSON responses with intelligent fallback parsing
- **BR-LLM-024**: MUST extract structured data elements (actions, parameters, conditions) from JSON responses
- **BR-LLM-025**: MUST provide response format validation with error-specific feedback

#### 5.1.6 Multi-Stage Action Generation
- **BR-LLM-026**: MUST generate structured responses containing primary actions with complete parameter sets
- **BR-LLM-027**: MUST include secondary actions with conditional execution logic (if_primary_fails, after_primary, parallel_with_primary)
- **BR-LLM-028**: MUST provide context-aware reasoning for each recommended action including risk assessment and business impact
- **BR-LLM-029**: MUST generate dynamic monitoring criteria including success criteria, validation commands, and rollback triggers
- **BR-LLM-030**: MUST preserve contextual information across multi-stage remediation workflows
- **BR-LLM-031**: MUST support action sequencing with execution order and timing constraints
- **BR-LLM-032**: MUST implement intelligent action parameter generation based on alert context and environment state
- **BR-LLM-033**: MUST provide confidence scoring for each action recommendation with supporting evidence

---

## 6. Context-Aware Decision Making

### 6.1 Business Capabilities

#### 6.1.1 Multi-Dimensional Context Integration
- **BR-AIDM-001**: MUST integrate alert context, system state, and historical patterns for decision making
- **BR-AIDM-002**: MUST preserve context across multi-stage remediation workflows
- **BR-AIDM-003**: MUST correlate context from multiple data sources (metrics, logs, traces)
- **BR-AIDM-004**: MUST adapt decision making based on environmental characteristics and constraints
- **BR-AIDM-005**: MUST maintain context consistency across provider failover scenarios

#### 6.1.2 Conditional Logic Processing
- **BR-AIDM-006**: MUST support complex conditional execution logic (if_primary_fails, after_primary, parallel_with_primary)
- **BR-AIDM-007**: MUST evaluate dynamic conditions based on real-time system state
- **BR-AIDM-008**: MUST implement time-based conditional execution with scheduling constraints
- **BR-AIDM-009**: MUST support nested conditional logic for complex remediation scenarios
- **BR-AIDM-010**: MUST provide conditional validation and error handling

#### 6.1.3 Workflow State Management
- **BR-AIDM-011**: MUST maintain workflow state across multiple execution stages
- **BR-AIDM-012**: MUST support workflow pause, resume, and rollback operations
- **BR-AIDM-013**: MUST track execution progress with stage-aware metrics and logging
- **BR-AIDM-014**: MUST implement workflow checkpointing for recovery scenarios
- **BR-AIDM-015**: MUST provide workflow state export and import capabilities

#### 6.1.4 Dynamic Monitoring and Validation
- **BR-AIDM-016**: MUST implement AI-defined success criteria monitoring
- **BR-AIDM-017**: MUST execute validation commands based on AI-generated criteria
- **BR-AIDM-018**: MUST trigger rollback actions when AI-defined conditions are met
- **BR-AIDM-019**: MUST adapt monitoring thresholds based on context and environment
- **BR-AIDM-020**: MUST provide real-time validation feedback to AI decision engines

---

## 7. Integration Requirements

### 7.1 Internal Integration
- **BR-INT-001**: MUST integrate with workflow engine for complex decision making
- **BR-INT-002**: MUST utilize vector database for similarity search and pattern matching
- **BR-INT-003**: MUST connect to action history repository for learning data
- **BR-INT-004**: MUST coordinate with monitoring systems for real-time metrics
- **BR-INT-005**: MUST integrate with intelligence components for enhanced analysis
- **BR-AI-TRACK-001**: MUST integrate with Remediation Processor tracking system
  - Receive alert tracking ID from Remediation Processor for all AI analysis operations
  - Include tracking ID in all AI service interactions (HolmesGPT-API, LLM providers)
  - Propagate tracking ID to Workflow Engine with analysis results and recommendations
  - Maintain AI decision audit trail linked to alert tracking for explainability
  - Support AI effectiveness measurement per alert tracking correlation
  - **Enhanced for Post-Mortem**: Record detailed AI reasoning, confidence scores, and alternative options considered
  - **Enhanced for Post-Mortem**: Capture context data, prompts, and model responses used in decision making
  - **Enhanced for Post-Mortem**: Log AI service performance metrics, latency, and error conditions
  - **Enhanced for Post-Mortem**: Store model version, configuration, and parameters used for reproducibility
  - **Enhanced for Post-Mortem**: Record human feedback and corrections to AI decisions for learning

### 7.2 External Integration
- **BR-INT-006**: MUST connect to multiple LLM provider APIs with failover capabilities
- **BR-INT-007**: MUST integrate with Kubernetes API for cluster state information
- **BR-INT-008**: MUST connect to monitoring systems (Prometheus, Grafana) for metrics
- **BR-INT-009**: MUST support webhook integration for real-time notifications
- **BR-INT-010**: MUST integrate with external analytics platforms for advanced insights

---

## 8. Performance Requirements

### 8.1 Response Times
- **BR-PERF-001**: AI analysis MUST complete within 10 seconds for standard alerts
- **BR-PERF-002**: Condition evaluation MUST complete within 3 seconds
- **BR-PERF-003**: LLM responses MUST be received within 30 seconds (including retries)
- **BR-PERF-004**: Effectiveness assessment MUST complete within 5 seconds
- **BR-PERF-005**: Insight generation MUST complete within 15 seconds

### 8.2 Throughput & Scalability
- **BR-PERF-006**: MUST handle minimum 50 concurrent AI analysis requests
- **BR-PERF-007**: MUST support 100 condition evaluations per minute
- **BR-PERF-008**: MUST process 1000 effectiveness assessments per hour
- **BR-PERF-009**: MUST maintain performance under peak load conditions
- **BR-PERF-010**: MUST implement horizontal scaling for increased demand

### 8.3 Resource Efficiency
- **BR-PERF-011**: CPU utilization SHOULD NOT exceed 70% under normal load
- **BR-PERF-012**: Memory usage SHOULD remain under 2GB per instance
- **BR-PERF-013**: MUST implement efficient caching to reduce API calls
- **BR-PERF-014**: MUST optimize prompt size to minimize token usage costs
- **BR-PERF-015**: MUST implement connection pooling for external API calls

---

## 9. Quality & Reliability Requirements

### 9.1 Accuracy & Precision
- **BR-QUAL-001**: AI analysis accuracy MUST exceed 85% validation threshold
- **BR-QUAL-002**: Recommendation relevance MUST maintain >80% user satisfaction
- **BR-QUAL-003**: Condition evaluation MUST achieve >90% accuracy rate
- **BR-QUAL-004**: False positive rate MUST remain below 5% for critical alerts
- **BR-QUAL-005**: MUST implement continuous accuracy monitoring and improvement

### 9.2 Reliability & Availability
- **BR-QUAL-006**: AI services MUST maintain 99.5% uptime availability
- **BR-QUAL-007**: MUST implement graceful degradation when external LLMs are unavailable
- **BR-QUAL-008**: MUST provide fallback decision making using cached patterns
- **BR-QUAL-009**: MUST recover automatically from transient failures within 60 seconds
- **BR-QUAL-010**: MUST maintain service continuity during model updates or changes

### 9.3 Data Quality & Validation
- **BR-QUAL-011**: MUST validate all input data before processing
- **BR-QUAL-012**: MUST sanitize and clean training data for learning algorithms
- **BR-QUAL-013**: MUST detect and handle data anomalies or corruption
- **BR-QUAL-014**: MUST maintain data lineage for AI decision traceability
- **BR-QUAL-015**: MUST implement data quality metrics and monitoring

---

## 10. Security Requirements

### 10.1 API Security
- **BR-SEC-001**: MUST secure all LLM provider API communications with TLS 1.3+
- **BR-SEC-002**: MUST implement API key rotation and secure storage
- **BR-SEC-003**: MUST validate and sanitize all prompts to prevent injection attacks
- **BR-SEC-004**: MUST implement rate limiting to prevent abuse
- **BR-SEC-005**: MUST monitor for suspicious API usage patterns

### 10.2 Data Protection
- **BR-SEC-006**: MUST encrypt sensitive data in AI processing pipelines
- **BR-SEC-007**: MUST implement data anonymization for non-production environments
- **BR-SEC-008**: MUST secure model training data and prevent unauthorized access
- **BR-SEC-009**: MUST implement secure model storage and version control
- **BR-SEC-010**: MUST provide audit trails for all AI-driven decisions

### 10.3 Privacy & Compliance
- **BR-SEC-011**: MUST comply with data protection regulations (GDPR, CCPA)
- **BR-SEC-012**: MUST implement data retention policies for AI training data
- **BR-SEC-013**: MUST provide data deletion capabilities upon request
- **BR-SEC-014**: MUST ensure AI models don't leak sensitive information
- **BR-SEC-015**: MUST maintain privacy-preserving analytics and reporting

---

## 11. Error Handling & Recovery

### 11.1 Error Classification
- **BR-ERR-001**: MUST classify AI errors by type (model, data, network, logic)
- **BR-ERR-002**: MUST implement severity-based error handling strategies
- **BR-ERR-003**: MUST distinguish between recoverable and non-recoverable errors
- **BR-ERR-004**: MUST provide detailed error context for troubleshooting
- **BR-ERR-005**: MUST implement error correlation across AI components

### 11.2 Recovery Strategies
- **BR-ERR-006**: MUST implement automatic retry with exponential backoff
- **BR-ERR-007**: MUST provide circuit breaker patterns for external AI services
- **BR-ERR-008**: MUST support graceful degradation to rule-based fallbacks
- **BR-ERR-009**: MUST implement model rollback capabilities for failed updates
- **BR-ERR-010**: MUST provide manual override capabilities for AI decisions

---

## 12. Monitoring & Observability

### 12.1 Performance Monitoring
- **BR-MON-001**: MUST track AI service response times and success rates
- **BR-MON-002**: MUST monitor model accuracy and drift over time
- **BR-MON-003**: MUST measure resource utilization and cost optimization metrics
- **BR-MON-004**: MUST track API usage and quota consumption across providers
- **BR-MON-005**: MUST provide real-time performance dashboards

### 12.2 Business Metrics
- **BR-MON-006**: MUST track decision accuracy and business outcome correlation
- **BR-MON-007**: MUST monitor effectiveness improvement trends over time
- **BR-MON-008**: MUST measure user satisfaction with AI recommendations
- **BR-MON-009**: MUST track cost savings achieved through AI-driven automation
- **BR-MON-010**: MUST provide business value metrics and ROI calculations

### 12.3 Alerting & Notifications
- **BR-MON-011**: MUST alert on AI service degradation or failures
- **BR-MON-012**: MUST notify on model accuracy drops below thresholds
- **BR-MON-013**: MUST alert on unusual resource consumption patterns
- **BR-MON-014**: MUST provide escalation procedures for critical AI failures
- **BR-MON-015**: MUST implement intelligent alerting to reduce noise

---

## 13. Data Management Requirements

### 13.1 Training Data Management
- **BR-DATA-001**: MUST maintain versioned training datasets with lineage tracking
- **BR-DATA-002**: MUST implement data quality validation for training inputs
- **BR-DATA-003**: MUST support incremental learning with new data integration
- **BR-DATA-004**: MUST provide data export capabilities for external analysis
- **BR-DATA-005**: MUST implement data retention and archival policies

### 13.2 Model Management
- **BR-DATA-006**: MUST maintain model versioning and rollback capabilities
- **BR-DATA-007**: MUST implement model performance benchmarking and comparison
- **BR-DATA-008**: MUST support A/B testing for model improvements
- **BR-DATA-009**: MUST provide model explainability and interpretability tools
- **BR-DATA-010**: MUST implement automated model retraining workflows

### 13.3 Knowledge Management
- **BR-DATA-011**: MUST maintain knowledge bases for domain-specific information
- **BR-DATA-012**: MUST implement knowledge graph construction and maintenance
- **BR-DATA-013**: MUST support knowledge base updates and versioning
- **BR-DATA-014**: MUST provide knowledge retrieval and augmentation capabilities
- **BR-DATA-015**: MUST implement knowledge validation and quality assurance

---

## 14. Success Criteria

### 14.1 Functional Success
- AI analysis provides relevant insights with >85% accuracy rate
- Recommendation engine generates actionable suggestions with >80% user acceptance
- Condition evaluation operates reliably with >90% success rate
- LLM integration supports all required providers with <2% failure rate
- Learning capabilities demonstrate measurable improvement over time

### 14.2 Performance Success
- All AI operations meet defined latency requirements under normal load
- System scales to handle peak demand with maintained quality
- Resource utilization remains within optimal ranges
- Cost optimization reduces LLM API expenses by 20% through efficiency gains
- Error rates remain below 1% for critical AI operations

### 14.3 Business Success
- AI-driven decisions result in measurable improvement in system reliability
- Effectiveness assessment shows positive trends in remediation success
- User satisfaction with AI recommendations exceeds 85%
- Operational efficiency gains demonstrate ROI within 6 months
- Knowledge accumulation enables increasingly sophisticated decision making

---

## 15. Release Versioning Strategy

### 15.1 Version 1 (v1) - HolmesGPT-Only Integration
**Scope**: Simplified AI Analysis Engine with single provider integration + AI Insights Service (graceful degradation)
**Timeline**: 3-4 weeks development
**Risk**: LOW - Single integration point with proven technology
**Update**: v2.1 (2025-01-02) - AI Insights Service moved from V2 to V1

#### v1 Core Requirements
- **Investigation Provider**: HolmesGPT-API integration only (BR-AI-011 to BR-AI-015 v1)
- **Fallback Mechanism**: Graceful degradation with error handling (BR-AI-024 v1)
- **LLM Integration**: HolmesGPT-API handles all LLM provider management (BR-LLM-001 to BR-LLM-005 v1)
- **Cost Optimization**: HolmesGPT-API internal optimization (BR-LLM-010 v1)
- **AI Insights Service** (**NEW V2.1**): Effectiveness monitoring with graceful degradation (BR-INS-001 to BR-INS-010 v1)
  - Week 5 deployment: "Insufficient data" status with low confidence (0-20%)
  - Week 8-10: Progressive capability improvement as data accumulates (40-60% confidence)
  - Week 13+: Full effectiveness monitoring with high confidence (80-95%)
  - 98% of business logic already implemented in `pkg/ai/insights/`

#### v1 Architecture Benefits
- ✅ **Reduced Complexity**: Single integration eliminates multi-provider routing logic
- ✅ **Faster Development**: 60-80% reduction in implementation complexity
- ✅ **Lower Risk**: Proven HolmesGPT reliability and well-documented API
- ✅ **Future Ready**: Clean abstraction interfaces enable v2 enhancements
- ✅ **Progressive Capability** (**NEW V2.1**): AI Insights Service provides immediate value with graceful degradation
- ✅ **Complete Business Logic** (**NEW V2.1**): 98% of AI Insights code already exists, only microservice wrapper needed

### 15.2 Version 2 (v2) - Multi-Provider with Intelligent Routing
**Scope**: Enhanced AI Analysis Engine with fallback mechanisms and cost optimization
**Timeline**: 6-8 weeks development (after v1 completion)
**Risk**: MEDIUM - Multi-provider complexity and intelligent routing logic
**Update**: v2.1 (2025-01-02) - AI Insights Service moved to V1, Intelligence Service remains in V2

#### v2 Enhanced Requirements
- **Multi-Provider Support**: Direct LLM integration fallback (BR-AI-011 to BR-AI-013 v2)
- **Fallback Mechanisms**: Direct LLM integration with context enrichment (BR-AI-024 v2)
- **Cost Optimization**: Intelligent model selection and alert complexity routing (BR-LLM-010 v2)
- **Alert Classification**: Complexity-based routing for cost optimization
- **Intelligence Service**: Advanced pattern discovery (BR-INT-001 to BR-INT-150) - enhances AI Insights with advanced patterns

#### v2 Architecture Enhancements
- ✅ **Resilience**: Multiple AI provider options with automatic failover
- ✅ **Cost Efficiency**: Intelligent model selection based on alert complexity
- ✅ **Performance**: Optimized provider selection for different use cases
- ✅ **Scalability**: Multi-provider load balancing and quota management

### 15.3 Implementation Strategy
```
Phase 1 (v1): HolmesGPT-only integration + AI Insights (graceful degradation) → Production deployment
Phase 2 (v2): Multi-provider enhancement + Intelligence Service + Post-Mortem Analysis → Advanced capabilities
Phase 3 (Future): Ensemble models and advanced AI orchestration
```

**Confidence Assessment**:
- **v1 Implementation**: 95% confidence for v1 implementation success (unchanged)
- **v1 AI Insights**: 92% confidence for graceful degradation approach (NEW V2.1)
- **v2 Enhancement**: 85% confidence for v2 enhancement success (unchanged)

---

## 16. Post-Mortem Analysis & Reporting (v2)

### 16.1 Business Purpose
**Version 2 Enhancement**: Provide intelligent, LLM-generated post-mortem reports based on comprehensive alert tracking data to enable continuous improvement, incident learning, and operational excellence.

### 16.2 Business Capabilities

#### 16.2.1 Automated Post-Mortem Generation
- **BR-POSTMORTEM-001**: MUST generate comprehensive post-mortem reports using LLM analysis of alert tracking data
  - **v2**: Analyze complete incident timeline from alert reception to resolution
  - **v2**: Correlate AI decisions, workflow executions, and human interventions
  - **v2**: Identify root causes, contributing factors, and resolution effectiveness
  - **v2**: Generate actionable insights and improvement recommendations

#### 16.2.2 Incident Analysis & Learning
- **BR-POSTMORTEM-002**: MUST provide detailed incident analysis with timeline reconstruction
  - **v2**: Reconstruct complete incident timeline with decision points and actions
  - **v2**: Analyze AI decision quality and identify improvement opportunities
  - **v2**: Evaluate workflow effectiveness and optimization potential
  - **v2**: Assess human intervention patterns and automation gaps

#### 16.2.3 Report Generation & Distribution
- **BR-POSTMORTEM-003**: MUST generate structured post-mortem reports in multiple formats
  - **v2**: Executive summary with key findings and business impact
  - **v2**: Technical deep-dive with detailed analysis and recommendations
  - **v2**: Action items with ownership, priority, and timeline
  - **v2**: Trend analysis comparing with historical incidents

#### 16.2.4 Continuous Improvement Integration
- **BR-POSTMORTEM-004**: MUST integrate post-mortem insights into system improvement
  - **v2**: Feed insights back into AI model training and decision optimization
  - **v2**: Update workflow templates based on effectiveness analysis
  - **v2**: Enhance monitoring and alerting based on incident patterns
  - **v2**: Improve automation coverage based on human intervention analysis

### 16.3 Data Requirements for Post-Mortem Analysis

#### 16.3.1 Alert Tracking Data Sources
- **Enhanced BR-SP-021**: Decision rationale, confidence scores, context data
- **Enhanced BR-HIST-002**: Performance metrics, error conditions, human interventions
- **Enhanced BR-AI-TRACK-001**: AI reasoning, model responses, alternative options
- **Enhanced BR-WF-ALERT-001**: Workflow execution paths, timing, effectiveness metrics

#### 16.3.2 Analysis Scope
- **Complete Incident Timeline**: From alert reception to final resolution validation
- **Decision Analysis**: AI reasoning quality, confidence accuracy, alternative evaluation
- **Execution Analysis**: Workflow effectiveness, performance metrics, failure points
- **Human Factor Analysis**: Intervention patterns, manual overrides, operator decisions
- **Business Impact Analysis**: Affected resources, service disruption, cost implications

### 16.4 Success Criteria (v2)
- **Report Generation**: <5 minutes for standard incidents, <15 minutes for complex incidents
- **Analysis Accuracy**: >90% accuracy in root cause identification and timeline reconstruction
- **Actionability**: >80% of generated recommendations result in measurable improvements
- **Adoption**: >95% of incidents have post-mortem reports generated and reviewed

---

## 17. AI Performance Optimization (V1 Enhancement)

### 17.1 Single-Provider Performance Optimization

#### **BR-AI-PERF-V1-001: Single-Provider Performance Optimization**
**Business Requirement**: The system MUST provide comprehensive performance optimization for single-provider AI scenarios (HolmesGPT-API) to ensure optimal response times and resource utilization.

**Functional Requirements**:
1. **Response Time Optimization** - MUST optimize AI analysis response times through intelligent caching and preprocessing
2. **Resource Utilization** - MUST monitor and optimize CPU, memory, and network resource usage
3. **Throughput Management** - MUST manage concurrent AI requests to maximize throughput without degradation
4. **Latency Reduction** - MUST implement strategies to reduce end-to-end analysis latency

**Success Criteria**:
- <10 second AI analysis response time for 95% of requests
- <5% CPU overhead for AI coordination and optimization
- Support for 100+ concurrent AI analysis requests
- 30% improvement in overall system throughput

**Business Value**: Enhanced user experience and operational efficiency through optimized AI performance

#### **BR-AI-PERF-V1-002: Investigation Quality Assurance**
**Business Requirement**: The system MUST implement comprehensive quality assurance mechanisms to ensure AI investigation results meet business standards and reliability requirements.

**Functional Requirements**:
1. **Quality Scoring** - MUST implement quality scoring algorithms for AI investigation results
2. **Confidence Validation** - MUST validate AI confidence scores against historical accuracy data
3. **Result Verification** - MUST implement automated verification of AI investigation findings
4. **Quality Feedback Loop** - MUST provide feedback mechanisms to improve investigation quality over time

**Success Criteria**:
- 90% quality score accuracy for AI investigation results
- 95% confidence score validation accuracy
- 85% automated verification success rate for AI findings
- Continuous quality improvement with measurable metrics

**Business Value**: Reliable AI investigations with 90% quality assurance and continuous improvement

#### **BR-AI-PERF-V1-003: Adaptive Performance Tuning**
**Business Requirement**: The system MUST provide adaptive performance tuning that automatically adjusts AI processing parameters based on system load, performance metrics, and business requirements.

**Functional Requirements**:
1. **Dynamic Parameter Adjustment** - MUST automatically adjust AI processing parameters based on performance metrics
2. **Load-Based Optimization** - MUST optimize processing strategies based on current system load
3. **Performance Monitoring** - MUST continuously monitor AI performance metrics and trends
4. **Predictive Scaling** - MUST predict performance needs and proactively adjust resources

**Success Criteria**:
- 25% improvement in AI processing efficiency through adaptive tuning
- 90% accuracy in load-based optimization decisions
- Real-time performance monitoring with <1 second metric updates
- 80% accuracy in predictive scaling decisions

**Business Value**: Self-optimizing AI system reduces operational overhead and improves performance

---

## 18. Multi-Provider AI Orchestration (V2 Advanced)

### 18.1 Advanced Multi-Provider Coordination

#### **BR-MULTI-PROVIDER-001: Provider Orchestration Intelligence**
**Business Requirement**: The system MUST provide intelligent orchestration across multiple AI providers (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Ollama) with capability-aware routing and cost optimization.

**Functional Requirements**:
1. **Capability Mapping** - MUST map different AI providers to their optimal use cases and capabilities
2. **Intelligent Routing** - MUST route requests to the most appropriate provider based on request characteristics
3. **Load Balancing** - MUST distribute load across providers to optimize performance and cost
4. **Provider Health Management** - MUST monitor provider health and automatically route around failures

**Success Criteria**:
- 95% accuracy in capability-aware routing decisions
- 30% cost reduction through intelligent provider selection
- <2 second provider failover time
- Support for 5+ simultaneous AI providers

**Business Value**: Optimized AI operations with 30% cost reduction and improved reliability

#### **BR-MULTI-PROVIDER-002: Ensemble Decision Making**
**Business Requirement**: The system MUST implement ensemble decision making that combines results from multiple AI providers to improve accuracy and reliability of AI-driven decisions.

**Functional Requirements**:
1. **Weighted Voting** - MUST implement weighted voting algorithms based on provider accuracy and confidence
2. **Consensus Analysis** - MUST analyze consensus across multiple providers for critical decisions
3. **Conflict Resolution** - MUST resolve conflicts between different provider recommendations
4. **Quality Assurance** - MUST ensure ensemble decisions meet quality and confidence thresholds

**Success Criteria**:
- 20% improvement in decision accuracy through ensemble methods
- 95% consensus accuracy for critical decisions
- 90% success rate in conflict resolution
- 85% quality assurance compliance for ensemble decisions

**Business Value**: Enhanced decision quality and reliability through multi-provider intelligence

#### **BR-MULTI-PROVIDER-003: Advanced Fallback Strategies**
**Business Requirement**: The system MUST implement sophisticated fallback strategies that maintain service quality and performance even when primary AI providers are unavailable or degraded.

**Functional Requirements**:
1. **Capability-Aware Fallback** - MUST implement fallback strategies based on provider capabilities
2. **Graceful Degradation** - MUST maintain service quality during provider failures
3. **Recovery Management** - MUST automatically recover and rebalance when providers return to service
4. **Performance Preservation** - MUST preserve performance characteristics during fallback scenarios

**Success Criteria**:
- <2 second fallback activation time
- 90% service quality preservation during fallback
- 95% automatic recovery success rate
- <10% performance degradation during fallback scenarios

**Business Value**: Reliable AI services with minimal disruption during provider failures

---

## 19. Advanced ML Analytics (V2 Advanced)

### 19.1 Machine Learning Performance Enhancement

#### **BR-ADVANCED-ML-001: ML Model Integration**
**Business Requirement**: The system MUST integrate advanced machine learning models for performance prediction, pattern recognition, and decision optimization to enhance AI-driven operations.

**Functional Requirements**:
1. **Performance Prediction Models** - MUST implement ML models to predict AI performance and resource needs
2. **Pattern Recognition** - MUST use ML for advanced pattern recognition in system behavior and failures
3. **Decision Optimization** - MUST optimize AI decisions using ML-based analysis of historical outcomes
4. **Model Management** - MUST provide comprehensive ML model lifecycle management

**Success Criteria**:
- 85% accuracy in performance prediction using ML models
- 90% accuracy in pattern recognition for system behavior
- 25% improvement in decision quality through ML optimization
- Complete ML model lifecycle management with versioning and rollback

**Business Value**: Enhanced AI capabilities through advanced ML integration

#### **BR-ADVANCED-ML-002: Consensus Optimization**
**Business Requirement**: The system MUST use machine learning to optimize consensus algorithms and improve the accuracy of ensemble decision making across multiple AI providers.

**Functional Requirements**:
1. **Consensus Algorithm Learning** - MUST learn optimal consensus algorithms from historical decision outcomes
2. **Weight Optimization** - MUST optimize provider weights based on performance and accuracy patterns
3. **Decision Quality Prediction** - MUST predict decision quality before consensus execution
4. **Continuous Learning** - MUST continuously improve consensus algorithms based on feedback

**Success Criteria**:
- 25% improvement in consensus accuracy through ML optimization
- 90% accuracy in decision quality prediction
- 95% success rate in weight optimization based on historical data
- Measurable continuous improvement in consensus performance

**Business Value**: Optimized ensemble decision making with improved accuracy and reliability

#### **BR-ADVANCED-ML-003: Cost Analytics and Optimization**
**Business Requirement**: The system MUST provide advanced cost analytics and optimization using machine learning to minimize AI operational costs while maintaining service quality.

**Functional Requirements**:
1. **Cost Prediction** - MUST predict AI operational costs based on usage patterns and provider pricing
2. **Optimization Algorithms** - MUST implement ML-based cost optimization algorithms
3. **ROI Analysis** - MUST provide detailed ROI analysis for AI investments and optimizations
4. **Predictive Scaling** - MUST predict and optimize resource scaling to minimize costs

**Success Criteria**:
- 40% ROI improvement through advanced cost analytics
- 85% accuracy in cost prediction models
- 30% cost reduction through ML-based optimization
- 90% accuracy in predictive scaling decisions

**Business Value**: Significant cost optimization with measurable ROI improvement

---

*This document serves as the definitive specification for business requirements of Kubernaut's AI & Machine Learning components. All implementation and testing should align with these requirements to ensure intelligent, reliable, and effective autonomous remediation capabilities.*
