Testing AI Agents: Performance Analysis and Evaluation

Building AI agents is only half the battle; ensuring they perform reliably, accurately, and cost-effectively is where the real challenge lies. As agentic workflows become standard in industry, knowing how to systematically test and analyze their behavior is an essential skill for modern developers and data professionals.\n\nIn this comprehensive text-based course, you will transition from simply writing basic prompts to designing, testing, and optimizing robust AI agents. You will gain a deep understanding of how to measure agent accuracy, track response latency, and manage operational costs to ensure production-grade reliability.\n\nWhat you'll learn:\n- Understand the foundational concepts of AI agents and how their performance is measured\n- Design rigorous testing frameworks to evaluate agent decision-making and tool usage\n- Apply modern observability practices to track latency, token usage, and execution costs\n- Implement benchmarking techniques to compare different model backends and prompt strategies\n- Analyze agent failure modes and systematically refine prompts for better reliability\n\nWe start with essential terminology and the core mechanics of AI agents before moving into practical testing methodologies and evaluation metrics. Through step-by-step written guides and structured code snippets, you will learn how to implement automated evaluations and interpret performance data.\n\nThis course is designed for aspiring data analysts, developers, and AI enthusiasts who want to learn how to evaluate intelligent systems. No prior experience with agent testing is required, though a basic familiarity with programming concepts is helpful.\n\nStart building and testing reliable AI agents today.