In this course, we will dive into what it truly means to build agentic AI—systems that perceive their environment, make decisions, learn from experience, and act autonomously. We begin by exploring the core principles of intelligent agency and how these systems interact with the world around them. From there, we will examine foundational models and techniques for crafting intelligent behavior, including decision trees, utility theory, Markov decision processes, and game theory. As the course progresses, you will design both standalone agents and complex multi-agent systems, learning how they make decisions, communicate, collaborate, and compete. We will also explore the human side of AI—how to build trust, ensure explainability, and design intuitive interfaces for effective human-AI collaboration. In the final phase, we investigate the frontier of AI: generative agents powered by large language and vision models. These advanced systems do not merely react—they reflect, plan, and interact in ways that resemble human cognition. You will gain hands-on experience building and evaluating these agents using state-of-the-art frameworks. By the end of the course, you will be equipped to model, build, and evaluate intelligent agents for both simulated and real-world environments, with a solid grounding in both theory and practice.