Evaluating Agentic Workflows for Reliable AI Assistants

Building AI assistants with agentic workflows is powerful, but ensuring they remain reliable in production is a major challenge. Without systematic testing, minor changes to your prompts or configurations can cause unexpected failures. This text-only course guides you through the foundational principles of evaluating complex, multi-step AI agents. You will learn how to identify failures, design reproducible test suites, and implement targeted fixes to ensure your customer support assistants perform consistently. What you'll learn: 1. Understand the core architecture of agentic workflows and why traditional software testing falls short. 2. Identify and bundle common agent failures, from infinite loops to incorrect tool usage. 3. Create reproducible evaluation datasets based on real-world customer support scenarios. 4. Apply modern evaluation techniques, including assertion-based checks and LLM-as-a-judge patterns. 5. Analyze agent traces to pinpoint exactly where a multi-step workflow broke down. 6. Implement systematic fixes through prompt engineering adjustments. You will start with key terminology and foundational definitions of agentic systems before moving into practical, step-by-step evaluation methodologies. Through written explanations and realistic code-based scenarios, you will learn to build a robust testing pipeline. This course is designed for software developers, product managers, and AI enthusiasts who are new to LLM evaluation and want to build dependable agentic systems; no advanced machine learning background is required. Start reading today to master the art of building reliable, production-ready AI agents.