Evaluating AI Agents: Systematic Testing and Iteration

Building an AI agent is only the first step; the real challenge lies in ensuring it performs reliably, safely, and accurately under real-world conditions. This text-based course guides you through the essential methodologies required to measure, analyze, and optimize the behavior of your AI agents. You will transition from ad-hoc manual testing to a structured, repeatable evaluation framework that ensures your agentic systems deliver consistent value. What you'll learn: Understand core evaluation concepts, benchmark metrics, and the fundamentals of agentic workflows; Define clear performance criteria and test cases tailored to specific agent tasks; Apply modern evaluation techniques, including LLM-as-a-judge patterns and programmatic assertions; Analyze failure modes, trace agent decision paths, and identify bottlenecks in multi-agent collaboration; Implement continuous evaluation practices to monitor and iterate on agents post-deployment. The course begins with foundational definitions of agentic architectures and evaluation metrics, before moving into practical testing scenarios, dataset creation, and systematic optimization strategies. This course is designed for software developers, data scientists, and product builders who are new to AI evaluation and want to build robust, production-ready agentic systems. Start reading today to master the art of systematic AI agent optimization.