Latent Space· Opinions· Nov 9, 2026, 05:23 GMT+8

How Stanford Teaches AI-Native Engineering: Interview with Mihail Eric

Original title: [One Million AI Engineer Special] How Stanford Teaches AI-Native Engineering — Mihail Eric, Monaco

Latent Space interviews Mihail Eric, who teaches Stanford's CS146S and leads AI at Monaco, in a special celebrating one million subscribers on the AI Engineer YouTube channel. The second run of CS146S brings in open-source maintainers, requiring students to learn an unfamiliar repo, propose a change, and explain the patch to the person who might merge it. The interview also covers Monaco's agent workspaces, an on-call agent with access to Datadog and GitHub, and how the team decides which tools to own versus buy; the full interview and the ten-week syllabus are available on YouTube.

Why it matters

The interview lays out CS146S's teaching design and Monaco's build-vs-buy tradeoffs for agent tooling, useful reference for engineering teams.

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What do you assign when a coding agent can finish last year’s homework in one prompt? In the second run of Stanford’s CS146S, Mihail Eric brought open-source maintainers into the course. Students have to learn an unfamiliar repo, propose a change, and ...

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We invited Mihail, who teaches The Modern Software Developer at Stanford and leads AI at Monaco, to celebrate one million subscribers on the AI Engineer YouTube channel. He opens up the course syllabus and shows us which lessons survived contact with his own engineering team. Monaco built its agent workspaces, bought its code review tools, and gave an on-call agent access to Datadog and GitHub. We ask how he decides which tools his team needs to own.

Watch the full interview and screenshares on YouTube.

Show notes

Stanford CS146S — The ten-week syllabus and assignments.
The Emperor Has No Clothes — Mihail builds a coding agent from scratch.
Demystifying Claude Code — Mihail traces a real agent harness.
Monaco’s agent workspaces — How the team sets up a workspace for each agent.
How to Kill the Code Review — Our essay on what happens when code generation outruns review.
Basis’s Authority Map — How agents distinguish current behavior from earlier design decisions.
Stripe’s Minions — A look inside Stripe’s background coding agents.
AI Engineer CODE Summit — The conference mentioned in the opening.

Timestamps

0:00 One million AI Engineer subscribers and the CODE Summit
0:51 Mihail Eric and Stanford CS146S
5:26 What junior engineers need to learn now
10:02 Open-source maintainers review the students’ code
15:04 A tour of the ten-week syllabus
24:04 The loop, tools and harness inside a coding agent
32:18 Context engineering for a large codebase
38:15 Skills that make you understand the diff
46:42 The Monaco.com dispute and the airplane banner
50:23 What Monaco does for sales teams
59:21 Inside Monaco’s engineering team
1:01:40 “You buy to learn, and you build to keep”
1:05:24 Making repos agent-ready and closing the on-call loop
1:12:37 When code review becomes the bottleneck
1:20:37 Security, dependencies and permissions
1:30:13 Background agents and Stripe’s Minions
1:34:16 MCP portals and why infrastructure converges
1:41:22 Building the software factory
1:45:24 What work is left for developers?
1:52:16 From personal adoption to team autonomy

Opening excerpt from The Thinking Mode, EO Studio.

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