Johns Hopkins University’s master’s degree program in Artificial Intelligence will launch a new course, Introduction to Agentic AI in fall 2025, expanding the program’s curriculum in the Whiting School of Engineering’s online and part-time Engineering for Professionals program. A 700-level follow-on course is planned for spring 2026, creating a comprehensive pathway for students to master autonomous AI system development.
The course addresses the growing field of agentic AI—systems that can perceive, decide, and act independently rather than simply recognizing patterns.
“As AI moves beyond passive tools to autonomous agents that can plan, reason, and act on their own, organizations need those who understand how to build and deploy these systems responsibly and effectively. The ability to create AI that can collaborate with people and other agents is becoming crucial across industries,” said Barton Paulhamus, chair of the program and chief of the JHU Applied Physics Lab’s Intelligent Systems Center.
Course Curriculum
The curriculum covers the essential components of agentic AI development. Students start with the theoretical foundations of intelligent agency, examining how AI systems process environmental information and make autonomous decisions. The course then moves through core methodologies including decision trees, utility theory, Markov decision processes, and game theory applications.
The program will also explore the human factors in AI deployment—building user trust, ensuring system explainability, and designing interfaces that facilitate productive human-AI interaction.
Advanced Applications
The latter portion of the course focuses on generative agents that utilize large language and vision models. These systems represent current AI capabilities, moving beyond reactive responses to demonstrate planning, reflection, and complex reasoning patterns. Students work directly with contemporary frameworks to build and evaluate these advanced agents.
Practical Outcomes
Course graduates will have practical experience in modeling, building, and evaluating intelligent agents for both simulated environments and real-world applications. The curriculum emphasizes hands-on development alongside theoretical understanding, preparing students to implement agentic AI solutions in professional settings.
Course Instructors
The course will be taught by Amir K. Saeed and Erhan Guven, bringing complementary expertise from industry and research.
Saeed is an applied research scientist at the Johns Hopkins University Applied Physics Laboratory. He holds degrees in mechanical engineering from UT Austin and data science from Johns Hopkins, with expertise in multimodal data fusion and reinforcement learning.
Guven is an AI scientist at Johns Hopkins University Applied Physics Laboratory. His research spans machine learning applications in cybersecurity, NLP, and bioinformatics. He holds a PhD in computer science from The George Washington University and has taught machine learning and related courses at Johns Hopkins and Loyola University Maryland.