In this lesson, we'll explore what sets agentic AI apart from traditional, prompt-based tools. You’ll dive into the foundational concepts behind agentic design — including initiative, autonomy, context retention, and human-AI collaboration. We’ll unpack real examples, compare agentic vs. reactive systems, and introduce key design frameworks that help you think beyond one-shot outputs. By the end of this lesson, you’ll be equipped with the vocabulary and mental models to identify and start shaping truly agentic experiences.

Agentic AI isn’t just about smart prompts — it’s about continuity, adaptability, and knowing when to take the next step. In this lesson, we'll move from theory to structure. You'll learn how to design multi-step workflows that incorporate user memory, dynamic branching, and graceful escalation. We'll break down real use cases into agentic flows using prompt chaining, decision points, and fallback strategies. You'll also explore how to embed personalization and keep interactions coherent over time. This is where you begin building agentic systems that actually work in the real world.

Now that you’ve designed agentic workflows, it’s time to stress-test and refine them. In this lesson, you'll learn how to evaluate whether your system builds trust, adapts to surprises, and maintains user control. We'll examine edge cases, explore how users might re-enter or deviate from your flows, and introduce techniques to integrate feedback loops — both human and system-driven. You'll leave this lesson with a clearer understanding of how to move from functional to resilient, responsible agentic AI.