Foundations of AI Agent Evaluation with LangSmith

As AI agents become more complex, ensuring they behave reliably in real-world scenarios is critical. Without proper testing and tracing, understanding why an agent failed or hallucinated can feel like guessing. This course provides a structured, written guide to evaluating AI agents using LangSmith. You will start with the foundational concepts of agentic AI and LLM behavior before moving into practical techniques for tracing execution paths, building datasets, and benchmarking performance. By the end of this text-based journey, you will know how to measure accuracy and reliability, giving you the confidence to move agent applications from prototype to production. What you'll learn: • Understand core AI agent terminology and why traditional software testing falls short. • Trace agent execution paths to debug complex prompts and tool calls. • Build and manage evaluation datasets to benchmark agent performance over time. • Apply modern evaluation patterns, including LLM-as-a-judge techniques. • Measure Retrieval-Augmented Generation (RAG) quality and agent reasoning steps. • Configure LangSmith projects to monitor production-ready agent workflows. The curriculum flows logically from basic definitions of AI agents to hands-on evaluation workflows, using clear written explanations and practical code snippets. You will read through step-by-step scenarios that illustrate how to catch errors and improve agent reliability. This course is designed for beginners and developers new to AI evaluation—no prior experience with LangSmith or advanced machine learning is required. Start reading today to master the essential skills for testing and benchmarking modern AI agents.