Foundations of AI Agent Observability with Langfuse

As AI agents and LLM applications become more complex, understanding exactly how they make decisions is critical. Without proper observability, debugging prompts and managing costs can feel like searching in the dark. This text-based course guides you through the essentials of monitoring and evaluating AI agents using Langfuse. You will explore how to capture execution traces, track token usage, and score model responses to ensure your artificial intelligence tools operate reliably and efficiently. What you'll learn: • Understand the core concepts of LLM observability and agent tracing. • Configure Langfuse to monitor AI agent workflows and capture execution steps. • Track token consumption and calculate costs for modern LLM applications. • Evaluate model outputs using scoring mechanisms and user feedback loops. • Analyze Retrieval-Augmented Generation (RAG) performance and prompt effectiveness. • Compare observability strategies to build robust, production-ready AI tools. The curriculum begins with foundational terminology and concepts before moving into practical, written exercises on implementing tracing and monitoring logic. You will read through step-by-step code snippets and scenarios that illustrate how to integrate these practices into your development workflow. This course is designed for beginners and aspiring developers; no prior experience with agentic AI or complex observability tools is required. Start reading today to gain clear visibility into your AI applications and build better agents.