Testing AI Agents: Building Evals from Prototype to Production

Building AI agents is exciting, but ensuring they reliably call the right tools and behave predictably in production is a major challenge. Without proper evaluation testing, agentic workflows can easily break or produce unexpected outputs. This text-based course guides you through the process of building robust evaluation systems (evals) for AI agents. You will learn how to define test cases, evaluate tool-calling capabilities, and establish a systematic testing pipeline that takes your agent from a simple prototype to a production-ready system. What you'll learn: Understand the foundational concepts of AI agent architectures and the critical role of evaluations; Create deterministic test cases to verify that your LLM calls the correct external tools based on user input; Implement evaluation scripts to measure agent performance, accuracy, and latency; Apply prompt engineering best practices to improve agent reliability and function calling; Structure a testing pipeline to automate evaluations before deploying updates. You will start with the fundamental theory of LLM evaluation before diving into step-by-step written guides and code snippets that demonstrate how to test tool-calling agents. This course is designed for aspiring AI engineers, software developers, and tech enthusiasts who want to learn how to test LLM applications, with no advanced machine learning background required. Start building reliable, production-ready AI agents today.