Overview

AI Observability in Action: Gain Full Visibility into Your LLMs, RAG, and Agent Workflows for Higher Confidence What happens when your AI fails, and you can't explain why? In a world where large language models, retrieval-augmented generation (RAG), and autonomous agents shape critical decisions, visibility is no longer optional, it's survival. AI Observability in Action is the definitive practical guide to understanding, tracing, and governing the behavior of complex AI systems in production. This book shows engineers, data scientists, and AI operators how to go beyond monitoring infrastructure metrics to observing model cognition, from token usage and grounding to latency, safety, and cost behavior. At its core, the book reveals how to establish an end-to-end observability pipeline for AI: Instrument every model, retrieval, and agent step with OpenTelemetry. Detect hallucinations and drift using real-time evaluation frameworks like DeepEval and Ragas. Track model decisions, memory loops, and tool invocations with traceable spans and structured events. Build actionable dashboards for quality, cost, and reliability across teams. Implement governance through auditable telemetry, data retention policies, and redaction strategies. Each chapter bridges theory with field-proven engineering practices, covering topics such as agent span design, policy block tracing, token cost control, and live evaluation pipelines. Readers will learn to diagnose high-latency RAG pipelines, detect runaway token spending, and establish observability boundaries that balance compliance with insight. Whether you manage enterprise AI infrastructure or build LLM-powered applications, this book equips you with the clarity and confidence to ask, and answer, the one question that matters most: ""What exactly is my AI doing?"" Build trust, reduce uncertainty, and bring your AI systems into full view. Get your copy of AI Observability in Action and start engineering visibility into every decision your AI makes.

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