Agentic AI MOOC, Fall 2025
Course Description
Agentic AI is the new frontier and is poised to transform the future of our daily lives with the support of intelligent task automation and personalization. In this course, we will first discuss fundamental concepts that are essential for Agentic AI, including the foundation of LLMs, reasoning, planning, agentic frameworks, and infrastructure. We will also cover representative agent applications, including code generation, robotics, web automation, and scientific discovery. Meanwhile, we will discuss limitations and potential risks of current LLM agents, and share insights into directions for further improvement.
Syllabus
LLM Agents Overview - Yann Dubois, OpenAI
Evolution of system designs from an AI engineer perspective - Yangqing Jia, NVIDIA
Post-Training Verifiable Agents - Jiantao Jiao, NVIDIA
Agent Evaluation & Project Overview
Some Challenges and Lessons from Training Agentic Models - Weizhu Chen, Microsoft
Multi-Agent AI - Noam Brown, OpenAI
Predictable Noise in LLM - Sida Wang, Meta
AI Agents to Automate Scientific Discoveries - James Zou, Stanford
Practical Lessons from Deploying Real-World AI Agents - Clay Bavor, Sierra
Multi-Agent Systems in the Era of LLMs - Oriol Vinyals, Google DeepMind
Autonomous Agents: Embodiment, Interaction, and Learning - Peter Stone, UT Austin / Sony AI
Agentic AI Safety & Security - Dawn Song, UC Berkeley
作者：Thomas