============================== 15-LAYER ENTERPRISE AI OBSERVABILITY ARCHITECTURE Domain: US Fintech HFT & Risk Analytics Stack: Datadog APM + Arize AI + LangSmith + Azure OpenAI GPT-4o SLA: < 200ms Latency | 99.9% Uptime | Real-time Hallucination Detection Compliance: SOC2 Type II | SEC | PII Masking ============================== ---------------------------------------- LAYER 1: EDGE TELEMETRY INGESTION & OPENTELEMETRY ---------------------------------------- Purpose: - Capture all inbound/outbound AI traffic at edge Components: - OpenTelemetry SDK (Agents on API Gateway, Services) - Sidecar collectors (Kubernetes DaemonSets) - Edge proxies (Envoy) Captured Signals: - Request/Response payloads (masked) - Latency, status codes - User/session metadata (hashed) Output: → Stream to Datadog + Kafka ---------------------------------------- LAYER 2: TOKEN METERING PIPELINE ---------------------------------------- Purpose: - Track token usage in real-time Metrics: - Prompt tokens - Completion tokens - Cost per request Pipeline: - Streaming token counters - Aggregation (per user, per service) Tools: - LangSmith + Custom middleware Latency Target: < 20ms overhead ---------------------------------------- LAYER 3: HALLUCINATION SCORING ENGINE ---------------------------------------- Purpose: - Detect hallucinations in real time Mechanisms: - Retrieval grounding check - Fact verification models - Confidence scoring (0–1) Signals: - Unsupported claims - Missing citations - Contradictions Output: { hallucination_score, confidence_score, flagged: true/false } ---------------------------------------- LAYER 4: VECTOR RETRIEVAL & SEMANTIC DRIFT ---------------------------------------- Purpose: - Monitor RAG performance Metrics: - Vector DB latency - Top-K relevance score - Embedding drift Detection: - Semantic mismatch vs query intent - Retrieval failure rate Tools: - Arize Phoenix / embedding analytics ---------------------------------------- LAYER 5: MODEL DRIFT, BIAS & CONCEPT SHIFT ---------------------------------------- Purpose: - Track model performance degradation Metrics: - Output distribution changes - Bias indicators (financial fairness checks) - Concept drift over time Methods: - Statistical drift detection (KS test) - Shadow evaluation pipelines ---------------------------------------- LAYER 6: TOKEN-LEVEL PII REDACTION TELEMETRY ---------------------------------------- Purpose: - Ensure compliance with SEC & SOC2 Mechanisms: - Regex + ML-based PII detection - Token-level masking Tracked Data: - Redaction count - PII categories (SSN, account, etc.) Logs: - Before/After masked payload hashes ---------------------------------------- LAYER 7: DISTRIBUTED TRACE & CALL TREE ---------------------------------------- Purpose: - Full observability across agent chains Components: - Distributed tracing (Datadog APM) - Span hierarchy (Agent → Tool → Model) Trace Example: User Query → Orchestrator → LLM → Tool → DB Metrics: - Span latency - Failure points ---------------------------------------- LAYER 8: SLA BREACH & ANOMALY ALERTING ---------------------------------------- Purpose: - Detect and respond to latency/quality issues Triggers: - Latency > 200ms - Hallucination score > threshold - Error rate spike Actions: - Alert (Datadog monitors) - Auto fallback routing - Circuit breaker activation ---------------------------------------- LAYER 9: LLM CACHE & DEDUP ANALYTICS ---------------------------------------- Purpose: - Optimize performance and cost Metrics: - Cache hit rate - Duplicate query detection - Latency savings Tech: - Redis semantic cache - Embedding similarity thresholding ---------------------------------------- LAYER 10: HITL FEEDBACK & TRAINING LOOP ---------------------------------------- Purpose: - Improve model accuracy continuously Sources: - Analyst feedback - Trader overrides Pipeline: - Feedback ingestion → Labeling → Fine-tuning dataset Tools: - LangSmith evaluation + Arize datasets ---------------------------------------- LAYER 11: RATE LIMITING & QUEUE TELEMETRY ---------------------------------------- Purpose: - Maintain system stability under load Metrics: - Request rate - Queue depth - Processing lag Controls: - Adaptive rate limiting - Priority queues (trading > analytics) ---------------------------------------- LAYER 12: DLP AUDIT & COMPLIANCE LOGGING ---------------------------------------- Purpose: - Maintain regulatory audit readiness Logs: - All prompts/completions (masked) - Access logs - Data lineage Features: - Immutable storage (WORM) - SEC audit-ready exports ---------------------------------------- LAYER 13: COST & UNIT ECONOMICS ---------------------------------------- Purpose: - Track AI spend efficiency Metrics: - Cost per query - Cost per trade decision - Token-to-revenue ratio Alerts: - Budget thresholds exceeded - Anomalous cost spikes ---------------------------------------- LAYER 14: RCA ENGINE & AUTO-REMEDIATION ---------------------------------------- Purpose: - Diagnose and fix issues automatically Capabilities: - Root cause detection (latency, drift, API failure) - Automated rollback - Model fallback routing Playbooks: - Predefined remediation workflows - Self-healing pipelines ---------------------------------------- LAYER 15: EXECUTIVE DASHBOARD & BENCHMARKS ---------------------------------------- Purpose: - Provide C-Suite visibility Dashboards: - SLA compliance % - Hallucination rate - Revenue impact of AI Benchmarks: - Latency trends - Accuracy trends - Uptime (target 99.9%) Tools: - Datadog dashboards - Custom BI layer ---------------------------------------- END-TO-END FLOW SUMMARY ---------------------------------------- 1. Edge Capture → Layer 1 2. Token Metering → Layer 2 3. Hallucination Detection → Layer 3 4. RAG Monitoring → Layer 4 5. Drift Monitoring → Layer 5 6. PII Redaction → Layer 6 7. Tracing → Layer 7 8. Alerts → Layer 8 9. Cache Optimization → Layer 9 10. Feedback Loop → Layer 10 11. Rate Control → Layer 11 12. Compliance Logging → Layer 12 13. Cost Tracking → Layer 13 14. RCA & Fix → Layer 14 15. Executive Insights → Layer 15 ---------------------------------------- SLA & PERFORMANCE STRATEGY ---------------------------------------- - Edge telemetry with minimal overhead - Real-time streaming analytics - Semantic caching (Redis) - Parallel tracing pipelines TARGET: ✔ Latency < 200ms ✔ Uptime 99.9% ✔ Real-time hallucination detection ---------------------------------------- ERROR HANDLING & GOVERNANCE ---------------------------------------- - Circuit breakers on SLA breach - Automatic fallback models - Full audit trace for every request - Compliance-first logging ---------------------------------------- FINAL OUTPUT FORMAT ---------------------------------------- { request_id, latency_ms, hallucination_score, pii_redacted: true/false, cache_hit: true/false, cost_usd, anomaly_detected: true/false, audit_log_id } ============================== ARCHITECTURE COMPLETE ==============================