Monitoring GenAI Systems — AWS AIP-C01 Task 4.3

mindmap
  root((Task 4.3<br/>Monitoring<br/>GenAI Systems))
    **4.3.1 Holistic Observability**
      Metrics Collection
        Infrastructure Metrics
        Service Metrics
        FM Operational Metrics
      Performance Tracing
        X-Ray Distributed Traces
        FM Interaction Traces
        Trace-to-Business Correlation
      Business Impact
        Revenue Attribution
        Conversion Tracking
        CSAT Correlation
      Custom Dashboards
        Executive Layer
        Operational Layer
        Debug Layer
    **4.3.2 GenAI Monitoring**
      Token Usage & Cost
        Per-Model Tracking
        Per-Intent Tracking
        Cost Anomaly CUSUM
      Prompt Effectiveness
        Version Registry
        A/B Comparison
        Effectiveness Scoring
      Hallucination & Quality
        Golden Dataset Scoring
        Quality Dimensions
        Degradation Alerting
      Anomaly Detection
        Token Burst Patterns
        Response Drift
        Isolation Forest
      Bedrock Invocation Logs
        Log Pipeline to Athena
        Request/Response Analysis
        Performance Benchmarks
    **4.3.3 Integrated Observability**
      Operational Dashboards
        Real-Time Widgets
        Alert Rules
        SLA Tracking
      Business Visualizations
        Revenue Attribution
        Funnel Analysis
        QuickSight Dashboards
      Compliance Monitoring
        PII Detection Pipeline
        Content Policy Tracking
        GDPR/SOC2 Evidence
      Forensic Traceability
        Immutable Audit Logs
        Request Replay
        Hash-Chain Integrity
      User & Model Behavior
        Session Analytics
        Behavior Clustering
        Abandonment Analysis
    **4.3.4 Tool Performance**
      Call Pattern Tracking
        Invocation Frequency
        Call Chain Analysis
        Dependency Mapping
      Performance Metrics
        Per-Tool Latency
        Success/Timeout Rate
        Parameter Accuracy
      Multi-Agent Coordination
        Handoff Tracing
        Context Transfer
        Overhead Metrics
      Usage Baselines
        Rolling Averages
        Anomaly Types
        Health State Machine
    **4.3.5 Vector Store Ops**
      Performance Monitoring
        Query Latency p50/p95/p99
        Throughput Metrics
        Index Health
      Index Optimization
        HNSW Auto-Tuning
        Segment Compaction
        Benchmark-Driven
      Data Quality
        Embedding Freshness
        Stale Content Detection
        Quality Scoring
    **4.3.6 FM Troubleshooting**
      Golden Datasets
        Stratified Design
        Automated Scoring
        Hallucination Detection
      Output Diffing
        Text Diff
        Semantic Diff
        Structured Field Diff
      Reasoning Tracing
        CoT Step Extraction
        Logical Error Detection
        Contradiction Finder
      Specialized Pipelines
        FM vs Traditional ML
        Pluggable Scoring
        Drift Detection

graph TB
    subgraph "Data Sources"
        BEDROCK[Amazon Bedrock<br/>Model Invocations]
        OPENSEARCH[OpenSearch Serverless<br/>Vector Store]
        ECS[ECS Fargate<br/>Orchestrator]
        APIGW[API Gateway<br/>WebSocket]
        DYNAMO[DynamoDB<br/>Sessions/Products]
        GUARDRAILS[Bedrock Guardrails]
    end

    subgraph "4.3.1 — Holistic Observability Layer"
        XRAY[X-Ray Traces]
        CW_METRICS[CloudWatch Metrics]
        CW_LOGS[CloudWatch Logs]
        EVENTS[EventBridge Events]
    end

    subgraph "4.3.2 — GenAI Monitoring Engine"
        TOKEN_MON[Token/Cost Monitor]
        QUALITY_MON[Quality Scorer]
        ANOMALY[Anomaly Detector]
        INVOC_LOGS[Invocation Log Analyzer]
    end

    subgraph "4.3.4 — Tool Performance"
        TOOL_TRACK[Tool Call Tracker]
        AGENT_TRACE[Agent Coordinator Tracer]
    end

    subgraph "4.3.5 — Vector Store Ops"
        VEC_MON[Vector DB Monitor]
        IDX_OPT[Index Optimizer]
        DATA_QUAL[Data Quality Validator]
    end

    subgraph "4.3.6 — FM Troubleshooting"
        GOLDEN[Golden Dataset Scorer]
        DIFFER[Output Differ]
        REASON[Reasoning Tracer]
    end

    subgraph "4.3.3 — Integrated Observability Platform"
        OPS_DASH[Operational Dashboards]
        BIZ_DASH[Business Impact Viz]
        COMPLIANCE[Compliance Monitor]
        AUDIT[Forensic Audit Trail]
        BEHAVIOR[User/Model Behavior]
    end

    BEDROCK --> XRAY & CW_LOGS & INVOC_LOGS
    OPENSEARCH --> VEC_MON
    ECS --> XRAY & CW_METRICS
    APIGW --> CW_METRICS & CW_LOGS
    DYNAMO --> CW_METRICS
    GUARDRAILS --> CW_LOGS & COMPLIANCE

    XRAY --> TOKEN_MON & TOOL_TRACK
    CW_METRICS --> ANOMALY & OPS_DASH
    CW_LOGS --> QUALITY_MON & AUDIT
    INVOC_LOGS --> ANOMALY & GOLDEN

    TOKEN_MON --> OPS_DASH & BIZ_DASH
    QUALITY_MON --> OPS_DASH & GOLDEN
    ANOMALY --> OPS_DASH & COMPLIANCE
    TOOL_TRACK --> AGENT_TRACE & OPS_DASH
    VEC_MON --> IDX_OPT & OPS_DASH
    DATA_QUAL --> GOLDEN & COMPLIANCE
    GOLDEN --> DIFFER --> REASON
    REASON --> BEHAVIOR

    style BEDROCK fill:#ff6b35,color:#fff
    style OPENSEARCH fill:#ff6b35,color:#fff
    style ECS fill:#ff6b35,color:#fff
    style OPS_DASH fill:#2ecc71,color:#fff
    style BIZ_DASH fill:#2ecc71,color:#fff
    style COMPLIANCE fill:#2ecc71,color:#fff
    style AUDIT fill:#2ecc71,color:#fff
    style ANOMALY fill:#e74c3c,color:#fff
    style GOLDEN fill:#e74c3c,color:#fff

graph LR
    USER[Customer] --> APIGW[API Gateway<br/>WebSocket]
    APIGW --> ECS[ECS Fargate<br/>Orchestrator]
    ECS --> BEDROCK[Bedrock Claude 3<br/>Sonnet/Haiku]
    ECS --> OPENSEARCH[OpenSearch<br/>Serverless<br/>Vector Store]
    ECS --> DYNAMO[DynamoDB<br/>Sessions/Products]
    ECS --> GUARD[Bedrock<br/>Guardrails]
    BEDROCK --> ECS
    OPENSEARCH --> ECS
    ECS --> APIGW --> USER

    style BEDROCK fill:#ff9900,color:#000
    style OPENSEARCH fill:#ff9900,color:#000
    style ECS fill:#ff9900,color:#000
    style DYNAMO fill:#ff9900,color:#000

pip install plotly pandas numpy scikit-learn