Anomaly Detection and LLM Diagnostic System
About This Architecture
Pluggable anomaly detection pipeline integrates GAM, Superset, and Prebid data sources through custom adapters feeding a Detection Engine with Baseline Calculator, Anomaly Detector, and Dedup & Correlator components. Severity Router triggers an LLM Agent (Claude Sonnet) executing a ReAct Loop that queries Superset, GitHub, Sentry, and GAM/Prebid tools to diagnose root causes before routing alerts to Teams, JIRA, and Slack. PostgreSQL stores baselines, anomalies, alerts, and a pgvector-powered knowledge base while GCS Bucket archives LLM transcripts and snapshots for audit trails. This architecture demonstrates production-grade ML observability combining statistical anomaly detection with agentic LLM reasoning for automated incident triage and response.
People also ask
How do I build an anomaly detection system with LLM-powered root cause analysis and automated alerting?
This architecture shows a Detection Engine with Baseline Calculator and Anomaly Detector feeding a Severity Router that triggers a Claude Sonnet ReAct agent. The LLM queries Superset, GitHub, Sentry, and GAM/Prebid tools to diagnose anomalies before routing to Teams, JIRA, Slack with PostgreSQL pgvector knowledge base.
- Domain:
- Ml Pipeline
- Audience:
- ML engineers building production anomaly detection systems with LLM-powered diagnostics
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