Agentic AI Content for Practitioners: Product

Overview

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"Agentic AI Content for Practitioners" is a hands-on course designed for practitioners seeking to design AI systems that adapt, build trust, and support real user goals. Through a blend of prompt-writing, memory-aware workflows, and trust-centered interaction design, learners will move from basic AI commands to agentic systems that behave more like collaborators than tools. The course features videos, real-world case studies, hands-on labs, and a capstone project that lets learners apply the full stack of agentic AI design principles. Whether designing onboarding flows or AI assistants, learners will walk away with frameworks and techniques for crafting adaptive, aligned, and human-centered AI experiences.

Syllabus

Lesson 1: What Makes AI “Agentic”? Foundations and Frameworks

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.

Lesson 2: Designing Adaptive Workflows: From Prompt Chains to Decision Trees

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.

Lesson 3: Evaluating Agentic Content: Feedback Loops, Edge Cases, and Refinement

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.