AI agents: Stanford's lecture 1, taken apart in four parts
A pepper shaker to put in a drawer. An agent that goes looking for milk in Des Moines, Iowa, without asking where you live. Another one that deletes emails because, while squeezing its memory, it judged the instruction to ask first unimportant. These three stories fit into a single Stanford lecture, and they say the same thing: we already know how to plug AI agents into your inbox or your credit card, but their reliability isn't keeping up. I took this lecture apart in four parts. Here's the map.
Guide, series overview. Source: lecture 1 of Stanford University's CS329Z course, "Engineering AI Agents", recorded on 23/09/2026 and posted by Stanford Online on 09/11/2026 according to YouTube. Facts about the course come from the video's description and the course website; everything else comes from the four parts, checked on 12/11/2026. The MCP specification was checked again for this overview on 12/11/2026 at 12:23 UTC.
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The course and its professor
CS329Z, "Engineering AI Agents", is a Stanford graduate course from fall 2026. It is taught by Diyi Yang, Michael Ryan and John Yang. Lecture 1 was recorded on 23/09/2026 and posted on 09/11/2026 by Stanford Online. On 12/11/2026 at 11:16 UTC, it had 171,261 views. More than an hour of class, in 14 official chapters: the last one starts at 1:01:05.
According to the video's description, Diyi Yang is an assistant professor in Stanford's Computer Science Department, also affiliated with the Stanford NLP Group (natural language processing), the Stanford HCI Group (human-computer interaction) and the Stanford Human-Centered AI Institute. Her research focuses on human-centred large language models and on human-AI interaction. She received the IEEE's AI 10 to Watch distinction (2020), a Microsoft Research Faculty Fellowship (2021), an NSF CAREER Award (2022) and a Sloan Research Fellowship (2024).
Why it matters, even if you don't code
An AI agent is an AI that doesn't just answer: it observes, decides, acts, starts again. The word is everywhere. This lecture tells you what the thing is made of.
No promises: Diyi Yang shows what worked, what fizzled, what still breaks. Her line that sticks, I'd put on every agent ad: the most powerful agent may not be the most reliable one.
The four parts
Four pieces of the lecture, in the order an agent is built.
- An AI agent is five parts and a loop. The real question: who decides the next step? The five parts of an agent, an idea that goes back to 1973, and the question that sorts everything: in a workflow, the code picks the next step; in an agent, the model does.
- BabyAGI and AutoGPT fizzled. What's left of 2023: the agent that thinks in a loop Think, act, observe, repeat: in the ReAct paper, hallucination accounts for 56% of chain of thought's examined failures, and 0% of ReAct's.
- AIs that debate, an AI that forgets the rule: multi-agent systems and memory, the Stanford lesson Making several AIs debate helped early models, but with today's models the professor no longer expects much of a performance gain from it; memory comes in three drawers, and that's where the instruction gets lost.
- MCP, the universal plug for AI agents. And a pop-up is enough to distract them From Toolformer to MCP, the standard plug between AI apps and tools; then what still breaks: faced with booby-trapped pop-ups, agents click on them 86% of the time on average.
My take: start with part 1, even if you think you know what an agent is. The question "who decides the next step?" then helps you read everything else, ads included.
Where to watch the lecture
The video is on Stanford Online's YouTube channel: lecture 1 on YouTube. Its description points to a playlist of the whole course. The CS329Z course website gives the lecture schedule and adds materials as they are released.
Words to remember
- AI agent: an AI that observes, decides, acts and starts again, instead of just answering.
- MCP (Model Context Protocol): shared rules for plugging an AI app into data and tools.
Source sheet
- Stanford Online: video "What Are AI Agents? Agentic Systems Explained | Stanford CS329Z Lecture 1 with Diyi Yang", posted on 09/11/2026, recorded on 23/09/2026; description and official chapters.
- Stanford CS329Z: course website, read on 12/11/2026 at 12:23 UTC.
- Model Context Protocol: versions published on the official GitHub repository of the specification, read on 12/11/2026 at 12:23 UTC; official blog post on the release schedule, published on 11/11/2026, read on 12/11/2026 at 12:35 UTC.
- The four parts of the series, with their own sources and "Source passages" tables.
- Links: the video · course website · MCP specification versions (GitHub)
Source passages
Video description copied on 12/11/2026 (sources/video/03, YouTube page, 171,261 views read at 11:16 UTC). Course website and MCP versions read on 12/11/2026 at 12:23 UTC (log: sources/.journal-curl.tsv). Facts taken from the parts refer to their tables, which hold the original quotes.
Show the 8 sources
| Ref. | Quote (original language) | URL |
|---|---|---|
| S1↑1↑2↑3↑4↑5↑6+2 | "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" ; "explains why the most powerful agent may not be the most reliable one" ; "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" … "1:01:05 Key challenges: reliability, safety & privacy" (14 chapters) ; "Watch the full CS329Z playlist" ; "Recorded 9/23/2026 in CS329Z: Engineering AI Agents, a graduate course at Stanford University taught by Diyi Yang, Michael Ryan, and John Yang." ; "publishDate":{"simpleText":"Nov 9, 2026"} ; "originalViewCount":"171261" | https://www.youtube.com/watch?v=DGI4vucR71w |
| S2↑1↑2↑3↑4 | "CS 329Z: Engineering AI Agents Stanford / Fall 2026" ; "Diyi Yang, Instructor Michael Ryan, Instructor John Yang, Instructor" ; "Lecture materials will be linked here as they are released." ; "1 Wed Sep 23 Foundations & Landscape Introduction — What Are Agentic Systems?" | https://cs329z.stanford.edu/ |
| S3↑1↑2↑3 | Published versions (GitHub API, 12/11/2026 12:23 UTC), newest first: "tag_name": "2026-07-28", "published_at": "2026-07-28T16:47:49Z" ; "2026-07-28-RC", "2026-05-29T12:51:22Z", "prerelease": true ; "2025-11-25" … (no version or pre-release after 28/07/2026) ; tags: "2026-07-28", "2026-07-28-RC", "2025-11-25" … | https://github.com/modelcontextprotocol/modelcontextprotocol/releases |
| S4↑1↑2↑3 | Part 1: five parts (slide p. 29: "LLM Core" ; "Planning & Reasoning" ; "Memory" ; "Tools" ; "Environment") ; 1973 (p. 19: "First indexed “AI use” is from Hewitt, Bishop and Steiger in 1973") ; workflow / agent (p. 56: "Workflow: code fixes the steps" ; "Agent: the LLM decides the steps") ; A [01:03:12] / B [01:08:12]: "The most powerful agent may not be the most reliable agent" | https://siliciumrose.com/articles/stanford-cs329z-lecture-1-agent-definition.html |
| S5↑1↑2↑3 | Part 2: ReAct paper, table 2: "Hallucination Hallucinated reasoning trace or facts 0% 56%" ; AutoGPT: "#14209 - AutoPilot action gating modes - Ask First, Auto, Unsupervised" (v0.8.2, 2026-09-30) ; "#14981 - Every block declares its effect; Auto judges cheap outward actions and asks first on costly ones" (v0.9.0, 2026-10-23) | https://siliciumrose.com/articles/stanford-cs329z-lecture-1-react-autogpt.html |
| S6↑1↑2↑3 | Part 3: debate and memory in three drawers (video, chapters 40:39 and 43:12) ; ScaleMAS: "continues to scale up to 128 agents" ; "[Submitted on 12 Oct 2026]" ; MFM: "a multimodal filesystem memory architecture" ; "[Submitted on 15 Oct 2026 ( v1)" | https://siliciumrose.com/articles/stanford-cs329z-lecture-1-multi-agents-memory.html |
| S7↑1↑2↑3 | Part 4: pop-ups: "leads to an attack success rate (the frequency of the agent clicking the pop-ups) of 86% on average" ; MCP draft: 4 changes since 23/09/2026 (28/10, 06/11 twice, 09/11) ; Instacart: "began searching for milk in grocery stores located in Des Moines, Iowa." | https://siliciumrose.com/articles/stanford-cs329z-lecture-1-tools-mcp.html |
| S8↑1↑2 | "Nov 11, 2026" ; "we’re moving MCP specification releases to a regular six-month schedule" ; "We’ll generally publish specification releases on the last Tuesday of March and September" ; "For the next release, we’re targeting March 30, 2027 - expect MCP version 2027-03-30 to land then." | https://blog.modelcontextprotocol.io/posts/mcp-release-cycle/ |
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