Description
Browse Stanford's online courses in Agentic AI: https://stanford.io/4AIRb2z
Lecture Overview:
What is an AI agent, and how is it different from an LLM workflow? In the first lecture of Stanford CS329Z: Engineering AI Agents (Fall 2026), Stanford professor Diyi Yang traces the history of agents from the 1973 actor model to ReAct and AutoGPT, breaks down the anatomy of a modern LLM agent (LLM core, planning and reasoning, memory, tools, and environment), and explains why the most powerful agent may not be the most reliable one. She also covers course logistics and the key challenges for agents: reliability, training, safety, and evaluation.
Speaker: Diyi Yang
Diyi Yang is an assistant professor in the Computer Science Department at Stanford University, also affiliated with the Stanford NLP Group, Stanford HCI Group and Stanford Human Centered AI Institute. Her research focuses on human-centered LLMs and human-AI interaction. She is a recipient of IEEE “AI 10 to Watch” (2020), Microsoft Research Faculty Fellowship (2021), NSF CAREER Award (2022), and a Sloan Research Fellowship (2024).
Chapters:
0:00 Welcome & instructor introductions
1:18 Course overview & logistics
7:40 A short history of AI agents
14:00 ReAct, BabyAGI & AutoGPT: the agent boom
19:06 Q&A: Will model capabilities saturate?
22:05 Anatomy of an LLM agent
28:19 Planning & reasoning: CoT, ReAct, Reflexion
36:52 Q&A: ReAct vs. chain of thought
40:39 Multi-agent debate & orchestration
43:12 Agent memory: episodic, semantic, procedural
46:30 Tool use: Toolformer & Gorilla
53:31 Compound AI systems: workflows vs. agents
58:41 Tool calls & Model Context Protocol (MCP)
1:01:05 Key challenges: reliability, safety & privacy
Course website: https://cs329z.stanford.edu/
Watch the full CS329Z playlist: https://youtube.com/playlist?list=PLPRcF5HR02_U&si=3Z4OYphngcmQ0GIy
Recorded 9/23/2026 in CS329Z: Engineering AI Agents, a graduate course at Stanford University taught by Diyi Yang, Michael Ryan, and John Yang.
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