AI Agent Evaluation: Designing Evals from Prototype to Production

Building an AI agent prototype is relatively simple, but ensuring it performs reliably in the real world is a major challenge. Without systematic evaluation, updating your prompt or model can easily break existing functionality. This text-only course provides a clear, step-by-step framework to design and run evals, giving you the confidence to deploy your AI agents to production. You will start by mastering foundational evaluation concepts and learning how to define what quality actually means for your specific use case. Through clear written explanations and practical code examples, you will explore how to measure agent behavior, accuracy, and safety. What you'll learn: 1. Understand foundational evaluation concepts and why traditional testing methods fail with non-deterministic AI. 2. Design quality metrics to assess task completion, accuracy, and response relevance. 3. Implement modern evaluation patterns, including LLM-as-a-judge and retrieval-augmented generation (RAG) metrics. 4. Measure safety, reliability, and guardrail effectiveness to prevent hallucinations and unwanted behaviors. 5. Transition your AI application from an experimental prototype to a robust, production-ready system. This course begins with essential terminology and core definitions before guiding you through establishing test datasets and setting up automated evaluation workflows. It is designed for software developers, product managers, and AI enthusiasts who want to build dependable AI systems. No advanced machine learning background is required. Start reading today to build AI agents you can confidently trust in production.