AI Agent Evaluation: Optimize Performance with Structured Experiments

Building an AI agent is only the first step; the real challenge lies in making its outputs reliable, cost-effective, and consistently accurate. Without structured testing, optimizing your agent's prompts and tools is just guesswork. This text-based course guides you through the foundational principles of evaluating and optimizing AI agents. You will learn how to move from ad-hoc testing to systematic, evidence-based experiments that track cost, latency, and quality. What you'll learn: - Understand foundational evaluation terminology and the core mechanics of AI agent behavior - Design robust evaluation metrics and scoring rubrics tailored to your specific use cases - Apply Git-based workflows to track prompt changes and experiment runs systematically - Implement modern evaluation patterns, including LLM-as-a-judge and basic automated testing pipelines - Analyze cost, latency, and quality trade-offs to make informed optimization decisions - Practice evaluating retrieval-augmented generation (RAG) and tool-use accuracy through written scenarios The course begins with essential definitions and core testing methodologies before guiding you through hands-on text examples, code snippets, and structured written exercises. You will progress from manual scoring to designing automated evaluation pipelines that fit seamlessly into development workflows. This course is designed for developers, product managers, and AI enthusiasts who are new to AI evaluation and want to build reliable systems. No advanced machine learning background is required. Start building more reliable, efficient, and predictable AI agents today.