AI Agent Observability: LangSmith and Langfuse

As AI applications grow more complex, understanding how your agents make decisions is critical. Tracing and monitoring these workflows is the key to building reliable AI systems. This course provides a clear, text-based guide to AI observability, helping you compare and implement LangSmith and Langfuse. You will learn how to track token usage, debug complex Retrieval-Augmented Generation (RAG) pipelines, and monitor agent performance. What you will learn: • Understand foundational concepts of AI observability, LLM tracing, and telemetry. • Compare the core features, strengths, and use cases of LangSmith and Langfuse. • Learn how to instrument your code to capture agent thoughts and actions. • Practice evaluating LLM outputs and managing prompt versions effectively. • Analyze modern tracing patterns for RAG applications and multi-agent systems. • Choose the right observability stack for your specific project requirements. The curriculum begins with essential terminology and basic telemetry concepts before moving into practical, written implementation guides for both tools. You will read through clear explanations and code snippets to understand how tracing works under the hood. Designed for beginners and developers new to AI monitoring, this course requires no prior experience with observability platforms. Start reading today to gain full visibility into your AI agents.