Announcement:

Learn more and sign up for the AgentX - AgentBeats Competition here.

Prospective Students

Course Staff

Instructor (Guest) Co-instructor
Dawn Song Xinyun Chen
Dawn Song Xinyun Chen
Professor, UC Berkeley Research Scientist, Meta

Teaching Staff: Xiuyu Li, Baifeng Shi, Chenyang Wang, Arhaan Aggarwal

Guest Speakers

Yann Dubois
Yann Dubois
Member of Technical Staff
OpenAI
Yangqing Jia
Yangqing Jia
VP, Al System Software
NVIDIA
Jiantao Jiao
Jiantao Jiao
Director of Research & Distinguished Scientist
NVIDIA
Weizhu Chen
Weizhu Chen
Technical Fellow & CVP
Microsoft
Noam Brown
Noam Brown
Research Scientist
OpenAI
Sida Wang
Sida Wang
Research Scientist
Meta
James Zou
James Zou
Professor
Stanford
Clay Bavor
Clay Bavor
Co-Founder
Sierra
Oriol Vinyals
Oriol Vinyals
VP, Research
Google DeepMind
Peter Stone
Peter Stone
Chief Scientist at Sony Al, Professor at UT Austin
Sony AI

Class Time and Location

Lecture: 3-5pm PT Monday at Valley Life Sciences 2050

Course Description

Agentic AI is the new frontier and poised to transform the future of our daily life 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, reasonsing, 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

Date Lecture
(3:10PM-5:00PM PT)
Supplemental Readings
Sep 8 Introduction
Dawn Song, UC Berkeley
[Slides]
Sep 15 LLM Agents Overview
Yann Dubois, OpenAI
[Slides] [Recording]
- KIMI K2: Open Agentic Intelligence
- DeepSeek-V3 Technical Report
Sep 22 Evolution of system designs from an AI engineer perspective
Yangqing Jia, NVIDIA
[Slides] [Recording]
Sep 29 Post-Training Verifiable Agents
Jiantao Jiao, NVIDIA
[Slides] [Recording]
- Introducing SWE-bench Verified
- BrowseComp: a benchmark for browsing agents
Oct 6 Agent Evaluation & Project Overview
[Slides] [Recording]
- Survey on Evaluation of LLM-based Agents
Oct 13 Some Challenges and Lessons from Training Agentic Models
Weizhu Chen, Microsoft
[Slides] [Recording]
Oct 20 Multi-Agent AI
Noam Brown, OpenAI
[Slides] [Recording]
Oct 27 Predictable Noise in LLM
Sida Wang, Meta
[Slides] [Recording]
- Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations
Nov 3 AI Agents to Automate Scientific Discoveries
James Zou, Stanford
[Livestream]
- The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies
- Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents
Nov 10 Clay Bavor, Sierra
Nov 17 Oriol Vinyals, Google DeepMind
Nov 24 Peter Stone, UT Austin / Sony AI
Dec 1 Agentic AI Safety & Security
Dawn Song, UC Berkeley
Dec 8 No lecture — RRR week

Enrollment and Grading

Prerequisites: Students are strongly encouraged to have had experience and basic understanding of Machine Learning and Deep Learning before taking this class, e.g., have taken courses such as CS182, CS188, and CS189.

Please fill out the petition form if you are on the waitlist or can’t get added to the waitlist.

This is a variable-unit course. Detailed grading breakdown will be released soon!

Grading

1 unit 2 units 3/4 units
Participation 40% 30% 20%
Quizzes 20% 10% 10%
Article 40%
Project Sum: 60% Sum: 70%
Phase 1 Sum: 45% Sum: 50%
Proposal 5% 5%
Early demo (2-min video & milestone report) 10% 10%
Final Green Agent Submission 30% 35%
Phase 2 Sum: 15% Sum: 20%
White agent implementation 10% 15%
White agent report (1–2 pages) 5% 5%

Bonus will be awarded for participating in AgentX-AgentBeats Competition.

Announcement for 1-Unit Students: You may choose to write an article or complete Phase 1 of the Agent Track. Either option is worth 40% of your grade. If you choose the Agent Track, coding requirements are minimal—building a simple agent (e.g., via prompt engineering) is sufficient.

Project Timeline

Released Due
Project group formation 9/15 9/22
Phase 1
Green agent proposal 9/27 10/8
Green agent demo submission & short report 10/7 10/20
Green agent submission — implementation, documentation & recording 11/7 11/17
Phase 2
White agent final submission — implementation & report 11/24 12/12

Article Timeline

Article (for 1-unit students) is due on 12/7.

Office Hours