Stanford's CS146S opens its AI-native engineering syllabus as coding agents finish homework in one prompt
Latent Space · rss · 2026-11-09
Latent Space interviews Mihail Eric (Stanford CS146S instructor and Monaco's AI lead) to celebrate AI Engineer hitting one million YouTube subscribers, unpacking both the course and his team's practices.
Stanford CS146S design
- Core question: what to assign when a coding agent completes last year's homework in one prompt?
- New this term: open-source maintainers join the course — students must learn an unfamiliar repo, propose a change, and defend the patch to the person who might merge it.
- The ten-week syllabus covers the coding agent loop, tools and harness, context engineering for large codebases, and skills for understanding diffs.
Monaco's agent engineering
- Dedicated workspaces per agent; code review tools are bought while agent workspaces are built in-house — "you buy to learn, and you build to keep."
- An on-call agent has access to Datadog and GitHub, closing the on-call loop.
- Making repos agent-ready, and spotting when code review becomes the bottleneck as generation outruns review.
Other highlights
- A traced walkthrough of a real Claude Code agent harness.
- Discussion of Stripe's background coding agents (Minions), MCP portals, and why infrastructure converges.
- Closing on what work remains for developers and moving from personal adoption to team autonomy.
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