94-815 Agentic Technologies
6 units, Carnegie Mellon University
In the rapidly evolving landscape of technology and data science, the ability to model and simulate complex systems is paramount. The growth of Large Language Models is also resulting in generative agents that combine the capabilities of LLMs with reasoning, planning, and other traditional AI areas to further advance the discipline of single-agent and multi-agent systems. This course on Agent-based Modeling and Digital Twins offers a comprehensive deep dive into the world of system thinking, enabling students to discern when and how to deploy agent-based simulations and digital twins as effective solutions. Starting from traditional single-agent and multi-agent models we will explore digital twins and the emerging area of generative agents. Drawing from real-world scientific, social, and business challenges, participants will master the art of designing, constructing, and simulating intricate agent-based models. They will also gain proficiency in bridging the gap between the physical and digital worlds, creating digital twins that can be calibrated, monitored, and utilized to evaluate policy decisions, model behavioral interventions, and understand human behaviors. Beyond foundational knowledge, this course extends its reach to practical applications, guiding students in harnessing the power of agent-based models across diverse sectors. From manufacturing to healthcare, automotive to aviation, and utilities to agriculture, participants will discover the transformative potential of digital twins. Students will develop specific use cases of digital twins in these industry sectors. As we delve deeper, the course will also explore the cutting-edge realm of generative agents, focusing on those employing LLMs. These agents, capable of logical reasoning, planning, optimization, and executing goal-driven tasks, represent the future of agent-based modeling.
Prerequisites: none listed
Offered: Spring 2027, Spring 2026
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