Building Agentic AI Applications with LLMs
NVIDIA and the University of Utah Scientific Computing and Imaging Institute and the Nucleus Institute are pleased to invite you to attend an upcoming hands-on technical training workshop:
Workshop Name: Building Agentic AI Applications with LLMs
Date: Friday, November 13, 2026
Time: 9:00 a.m. to 5:00 p.m. MDT
Location: On-campus (no virtual attendance option) at the University of Utah; location information will be provided by email to registrants in advance of the workshop (registration is required)
Host: Majid Memari, Assistant Professor of Computer Science, Utah Valley University
This training is offered exclusively to verifiable industry professionals. Please use your company email address when registering. Registrants using personal email addresses will not receive further details about the event.
About This Workshop:
The bar for what AI-powered agents can do has been steadily rising over the past few years, and new innovations allow them to not only engage in conversations but also utilize tools, conduct research, and execute on complex objectives at scale. This course empowers you to develop sophisticated agent systems that can execute on deep thought, research, software calling, and distributed operation. Throughout the course, you'll gain hands-on experience in designing agents that efficiently retrieve and refine information, intelligently route queries, and execute tasks concurrently using orchestration tools like LangGraph and sound software engineering practices. By the end of the course, you will have a solid foundation in agent architectures and will be able to construct interesting agent-like integrations to complement your existing workflows and software stacks.
Course Concepts:
Fundamentals of Agent Abstraction and LLMs
Structured Output and Basic Fulfillment Mechanisms
Retrieval Mechanisms and Environmental Tooling
Multi-Agent Systems and Frameworks
Final Assessment
Optional: Real-Time Agents
Prerequisites:
Introductory deep learning knowledge (including attention mechanisms and transformers). Experience from DLI’s Getting Started with Deep Learning or Fundamentals of Deep Learning is preferred.
Intermediate Python proficiency (including object-oriented programming and familiarity with ML libraries). Tutorials like Python Tutorial (w3schools.com) or equivalent practical experience suffice.