AI Agent Evaluation and Testing with the ADK Framework

How do you know if your AI agents are performing reliably, safely, and accurately? As autonomous systems become more common, establishing clear, automated evaluation criteria is essential to ensure they handle complex user interactions without failing. In this text-only course, you will learn how to design, implement, and run systematic evaluations for AI agents using the Agent Development Kit (ADK) framework. You will transition from manual, ad-hoc testing to automated, scalable assessment pipelines that ensure your agents behave as expected. What you will learn: - Understand the core concepts of AI agent evaluation and the role of the ADK framework. - Configure automated criteria to measure agent correctness, logical reasoning, and response quality. - Implement safety and guardrail assessments to prevent harmful or off-topic outputs. - Apply LLM-as-a-judge patterns to evaluate open-ended conversational flows. - Analyze evaluation logs to identify performance bottlenecks and refine agent prompts. - Design regression testing workflows to ensure updates do not break existing agent capabilities. This course begins with foundational definitions of agent evaluation and the core structure of the ADK framework. From there, you will read through step-by-step implementation guides, exploring practical testing scenarios, safety checks, and performance logging without needing advanced programming experience. This course is designed for beginners, developers, and product managers who want to understand how to test and validate AI agents systematically. No prior experience with agent evaluation frameworks is required. Start building safer, more reliable autonomous systems by mastering modern AI evaluation frameworks today.