Nov 8, 2026 · 2h 1m
Stanford redesigns software engineering for the agent era
[One Million AI Engineer Special] How Stanford Teaches AI-Native Engineering — Mihail Eric, Monaco
As coding agents absorb more implementation work, the episode examines which engineering skills remain essential and how teams must redesign codebases, review, security, and infrastructure.
- 1Foundational software engineering still matters, but students increasingly need judgment about context, verification, and delegation.
- 2Stanford’s CS146S replaces simplified assignments with open-source work and a curriculum spanning agents, security, review, and software factories.
- 3Monaco’s internal tools show how agent-ready codebases, background agents, and domain expertise can turn AI engineering ideas into operating systems.
Don't miss
Eric’s explanation of the software factory reframes engineering around human intent, review, and system judgment rather than raw pull-request volume.
The brief
Mihail Eric explains why Stanford created CS146S as coding agents rapidly changed software development, then kept refreshing the course as those systems grew more capable.
The course moves beyond toy assignments into open-source contributions, teaching students to work inside complex codebases while preserving foundational understanding rather than blindly accepting generated code.
Its ten-week curriculum covers context engineering, agent-ready codebases, skills, security, background agents, code review, and software factories—mixing established practice with open research questions.
At Monaco, Eric applies those ideas through an AI-native sales platform and internal systems including Monocoder and HAL, which extend agents across engineering and operational roles.
The central bottleneck is no longer code production alone: rapid agent output makes review, verifiability, security, and reliable DevOps the conditions for scaling software factories.
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