Overview

AI Observability and Tracing in Production: Tools, Patterns, and Best Practices When your LLM misbehaves, latency spikes, or an agent goes off-script, can you see why in seconds-or do you guess for hours? Teams shipping AI at scale share the same goal: reliable, explainable, cost-efficient systems. The obstacle is visibility. This book shows a practical, end-to-end approach to AI observability and tracing in production. You'll learn how to instrument LLMs, RAG pipelines, and multi-agent workflows; compare model versions safely; measure quality and drift; and investigate incidents with confidence. The guidance is concrete: metrics, logs, traces, spans, sessions, feedback loops, plus real patterns using OpenTelemetry, specialized AI platforms (e.g., Langfuse, Helicone, Vellum, Arize), and APM extensions from Datadog and New Relic. What you'll learn and do Instrument prompts, completions, embeddings, retrievals, and tool calls with the right granularity. Build traceable execution graphs for agents and RAG, with model/version, context lineage, and cost/latency attribution. Select and integrate tooling (self-hosted or SaaS) while meeting privacy, compliance, and audit needs. Design dashboards and alerts that catch drift, anomalies, and regressions-not noise. Run A/B and canary releases with observability comparisons and fast rollback criteria. Reduce spend with token accounting, caching, sampling, and smart retention policies. Investigate production issues methodically: correlate spans, isolate root causes, and write durable runbooks. Apply safety guardrails, policy enforcement, and explainability traces to show ""why this output."" Prepare CI/CD pipelines so every release ships with tested observability, not hope. Clear, direct, and hands-on, this guide blends SRE rigor with ML realities. It speaks the language of AI monitoring, tracing, RAG observability, drift detection, incident response, and cost optimization-so you can scale LLMs and agents without losing control.

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