Evaluating AI Agents: A Practical Guide to Agent Testing

Building AI agents is only half the battle; ensuring they behave reliably in multi-turn conversations is where the real challenge lies. This text-based course guides you through the essential methodologies of evaluating AI agents, helping you write robust tests that verify your agent's decision-making process. You will transition from manually testing prompts to writing systematic evaluations that run automatically. By understanding how to isolate agent logic and simulate user interactions, you will build the confidence to deploy AI systems that perform consistently in production environments. What you'll learn: - Understand the core principles of AI agent architecture and multi-turn conversation flow - Configure mock executors to simulate external API calls and system environments - Apply systematic testing strategies using mock data to isolate agent behavior early - Design evaluation scenarios that test edge cases and unexpected user inputs - Implement modern testing practices to measure agent accuracy and reliability - Practice writing clean, maintainable evaluation scripts using Python testing patterns. This course begins with foundational concepts of agent evaluation before walking you through the process of setting up mock environments, writing test scenarios, and analyzing evaluation results. Through clear explanations and practical code snippets, you will master the mechanics of agent testing. This course is designed for software developers, data scientists, and AI enthusiasts who are new to agent evaluation and want to build a reliable testing pipeline. A basic familiarity with Python is helpful, but no prior experience with AI testing frameworks is required. Start reading today to master the essential techniques for building reliable, well-tested AI agents.