AI Agent Evaluators: Fundamentals of Performance and Tool Scoring

Building AI agents is only half the battle; ensuring they behave reliably and select the correct tools is where the real challenge begins. To move past unpredictable trial-and-error, you need systematic ways to measure and score your agent's performance. This course guides you through the foundational principles of AI agent evaluation. You will transition from manual testing to designing automated evaluators that score agent outputs, validate tool selection, and ensure consistent results using deterministic JSON structures. What you'll learn: - Understand the core concepts of AI agent evaluation and why manual testing falls short - Design deterministic evaluators to score tool outputs against expected results - Implement tool selection score evaluators to verify your agent calls the correct functions - Apply structured JSON schemas to make agent outputs easily quantifiable and testable - Explore modern evaluation patterns, including LLM-as-a-judge and assertion-based testing You will start with essential terminology, learning how evaluators fit into the modern AI engineering lifecycle. From there, you will read through step-by-step explanations of scoring algorithms, tool validation logic, and automated assertion workflows. This course is designed for software developers, product builders, and AI enthusiasts who want to build reliable agentic workflows. No advanced machine learning background is required; basic programming logic and familiarity with JSON are all you need to get started. Start reading today to build AI agents you can confidently test, score, and deploy.