AI Agent Evaluation: Analyzing Runs and Performance Metrics

Building AI agents is only half the battle; the real challenge lies in understanding how they behave and measuring their success. Without systematic evaluation, it is impossible to know if changes to your agent's prompts or tools actually improve its performance. This course guides you through the foundational concepts of evaluating autonomous agents, helping you transition from guessing how your agents perform to analyzing run logs, measuring average scores, and tracking experiment history with confidence. What you'll learn: • Understand the core terminology and methodologies of AI agent evaluation. • Analyze individual run logs to trace decision-making paths and tool usage. • Calculate and interpret key performance metrics and average evaluation scores. • Establish naming strategies and experiment tracking workflows for your agent runs. • Implement modern evaluation patterns, including LLM-as-a-judge and automated benchmarks. • Identify failure patterns in agent behavior to systematically iterate and improve prompts. We begin with the foundational concepts of evaluation and agent tracing, ensuring you understand how agents make decisions. From there, you will read through step-by-step guidance on analyzing evaluation outputs, comparing experiment runs, and establishing repeatable testing workflows. This course is designed specifically for beginners, software developers, and AI enthusiasts who want to measure their systems effectively, with no advanced machine learning background required. Start reading today to bring structure, measurement, and reliability to your AI agent projects.