"LLM Observability with LangSmith: Tracing, Evaluating, and Monitoring Production LLM and Agent Applications"
Modern LLM and agent systems rarely fail in obvious ways; they drift, regress, over-spend, misroute tools, and disappoint users long before dashboards make the problem visible. This book is written for experienced AI engineers, platform teams, and senior developers who need a disciplined way to observe, measure, and improve production LLM applications. It treats LangSmith not as a convenience tool, but as a serious quality platform for operating complex systems under real production constraints.
Across the book, readers learn how to model traces, runs, and threads; instrument applications across frameworks and distributed services; analyze failures through trace inspection; design evaluation datasets and scoring strategies; run rigorous offline experiments; and monitor live traffic with online evaluators, human review, and automation rules. The emphasis is on production-grade practice: regression discipline, metadata design, cost-aware sampling, prompt versioning, alerting, retention policy, and the feedback loops that turn incidents into durable test assets.
The coverage is intentionally deep and operational. Rather than simplifying observability into generic monitoring advice, the book connects tracing, evaluation, CI gates, review workflows, and automated response into one continuous engineering system. Readers should already be comfortable with LLM application development, APIs, and modern software delivery practices; in return, they will gain a precise frame
