Testing AI Agents: Evaluating Multi-Tool Workflows for Production

Building an AI agent that works in a sandbox is easy, but ensuring it reliably uses the right tools in production is a major challenge. Without systematic testing, minor prompt changes can silently break your agent's decision-making logic. This text-based course guides you through the essential methodologies of agent evaluation (evals). You will learn how to design robust test datasets, measure the accuracy of multi-tool selection, and iteratively optimize your prompts to achieve production-grade reliability. What you'll learn: 1. Understand foundational AI agent evaluation concepts and core terminology. 2. Design evaluation datasets with diverse test cases to stress-test agent decision-making. 3. Test multi-tool workflows to ensure agents select and execute the correct tools. 4. Analyze evaluation scores to identify failure modes and agentic bottlenecks. 5. Refine system prompts and tool descriptions systematically based on quantitative data. 6. Implement modern observability and tracing concepts to monitor agent behavior. You will start with the fundamental principles of agent architecture and evaluation metrics before moving into hands-on testing scenarios. Through clear written explanations and practical code snippets, you will learn to run evals, interpret scores, and fine-tune your agent for real-world deployment. This course is designed for software developers, product builders, and AI enthusiasts who want to transition from basic prompt engineering to building robust, production-ready AI systems. No advanced machine learning background is required, as we start with the absolute basics. Start reading today to transition your AI agents from fragile prototypes to dependable production tools.