Tracing

Answer-first: Observability for enterprise MCP infrastructure demands unified OpenTelemetry GenAI semantic tracing across client prompts, gateway hops, and tool executions, combined with Prometheus latency histograms and cryptographically verified WORM audit ledgers. This distributed telemetry pipeline detects recursive agent tool execution loops within seconds, enforces strict latency SLAs, and ensures non-repudiable governance compliance for high-stakes autonomous workflows. ← Part 5: Production Security & OWASP MCP Top 10 | Next Chapter: Part 7: Enterprise Scaling & Governance → ...

📖 Bản tiếng Việt (Vietnamese Edition) Prerequisite: Familiarity with high-throughput inference engines and serving metrics covered in Part 8 — Inference Optimization: vLLM. Part 9 — Agentic Observability: OpenTelemetry, Tracing & Cost Monitoring Debugging traditional microservices involves tracking HTTP status codes, SQL query durations, and memory allocations. Debugging enterprise AI agent architectures requires tracking non-deterministic reasoning chains, token consumption surges, context window inflation, multi-turn tool loops, and subtle prompt drift. ...

Answer-first: Traditional Application Performance Monitoring (APM) tools fail to capture generative AI failure modes because an HTTP 200 response can still contain complete factual hallucinations, toxic responses, or $50.00 runaway token loops. Modern AI Observability implements OpenTelemetry GenAI Semantic Conventions v1.30+, correlating distributed multi-agent traces with token spend, Time-to-First-Token (TTFT), and automated LLM-as-a-Judge evaluation pipelines (Ragas / Phoenix). 📖 Bản tiếng Việt (Vietnamese Edition) | ← Series Hub | Next Chapter: Part 7: AI Security Engineering & DevSecOps → ...