This module explains how to design agentic AI interactions that are reliable and goal-directed. You’ll learn about the flipped interaction pattern, in which agents ask questions and guide the next steps; how multimodal inputs like screenshots can improve diagnosis; and when to use step-by-step execution versus ahead-of-time planning. The module also covers how to make agents work safely in real environments by providing the right context, constraining behavior with tools and actions, and using clear tool names, informative results, helpful error messages, and in-context examples to guide consistent performance.

This module introduces custom GPTs as a practical way to build agentic AI applications without traditional programming. You’ll learn how to shape model behavior using instructions, tone, background knowledge, and guardrails; how retrieval-augmented generation (RAG) brings in current or domain-specific information without retraining; and how custom GPTs combine instructions, knowledge, and capabilities to create reusable AI tools.