Distributed Tracing for LLM Observability: Tracking Costs, Hallucinations & Agent Reasoning

Description

Distributed Tracing for LLM Observability and Governance: Tracking Costs, Hallucinations & Agent Reasoning The Problem: Running a sophisticated AI analytics platform with zero visibility into decision-making, cost attribution, or failure detection. When agents hallucinate, tools fail silently, or APIs return unexpected data, systems burn through budgets producing incorrect outputs with no early warning or root cause traceability. The Solution: Comprehensive observability infrastructure capturing the complete AI reasoning lifecycle across distributed services, enabling detection of hallucinations, tool failures, inefficient patterns, and cost anomalies. Technical Architecture: Distributed Tracing: Custom correlation engine tracking requests across ASP.NET Core backend, Python MCP servers, and Claude API calls using OpenTelemetry patterns Multi-Stage AI Workflow Capture: Pre-execution planning, tool orchestration via Semantic Kernel, and post-execution self-critique - each stage logged with full reasoning context Elasticsearch Aggregation Pipeline: Transforms 15+ raw events per user query into enriched trace documents with complete reasoning chains Real-Time Cost Attribution: Token-level granularity revealing $0.001-$0.15 per query with breakdown by reasoning stage Live Reasoning Stream: SignalR integration showing AI thought process in real-time Anomaly Detection: Captures tool failures, API errors, hallucinations, and reasoning inconsistencies with full investigation context Key Discoveries: Hallucination Detection: AI post-reasoning caught fabricated metrics ("correlation of 2 is impossible") enabling root cause analysis of ambiguous tool outputs Self-Critique Reveals Inefficiencies: "correlation_analysis and kmeans_clustering were unnecessary for this histogram task" 40% Tool Redundancy Identified through post-execution analysis patterns Cost Optimization: Reduced average query cost from $0.05 to $0.02 by eliminating wasteful API calls Failure Traceability: Tool errors, API timeouts, and data quality issues captured with complete reasoning context Full Audit Trail for governance and compliance requirements