INFORMS San Francisco Chapter

### Abstract

As AI systems evolve from static models into autonomous collaborators, Agentic AI systems capable of reasoning, planning, and coordinated decision-making has become an essential skill for both students and practitioners.
This masterclass introduces participants to multi-agent systems and their deep intersection with Operations Research (OR). Attendees will explore how intelligent agents can tackle complex, real-world challenges such as dynamic scheduling, logistics optimization, and resource allocation; while adapting to uncertainty and operational constraints.
The session blends conceptual foundations with hands-on labs, guiding participants through the design, orchestration, and evaluation of agent workflows using practical frameworks and open-source tools. Whether you are a student building your first agent-based simulation or a professional integrating agentic decision systems into enterprise environments, this workshop offers a rigorous, applied learning experience bridging OR theory with modern AI practice.

This workshop will be led by Dhanashree Lele | Dr. Hongbo Dong | Dr. Shubhangi Deshpande | Dr. Krishna Malladi

### Key Takeaways

• Understand the foundations of Agentic and Multi-Agent AI and how they extend classical OR-based problem-solving.
• Live demonstration of how can AI help us make sense of opaque optimization results in automotive supply-chain planning (led by Hongbo Dong).
• Hands-on coding labs simulating intelligent planning, reasoning, and optimization under real-world constraints.
• Practical exposure to open-source agent frameworks, APIs, and orchestration strategies used in research and industry.
• Insights into emerging career and research pathways at the intersection of AI Agents, OR, and decision analytics.

This workshop is online. After registration, you will receive connection info near the event date.