How Stanford Teaches AI Coding Now

What does it take to build AI agents that deliver useful, reliable work?

Stanford lecturer and Monaco Head of AI Mihail Eric joins Demetrios for a conversation inside a Waymo about how AI is changing software development - and what it takes to put agents into production.

Mihail explains why he rebuilt most of his Modern Software Developer course, how he teaches students to avoid shipping AI slop, and why research, planning, and verification still matter as coding agents become more capable.

They discuss Claude Code, agent loops, context engineering, memory and compaction, and how to design MCP tools around what an agent needs to accomplish. Then they go inside Monaco’s approach to sales agents, from turning meetings and emails into useful insights to building a custom agent harness.

Along the way: lessons from Boris and Lee Robinson’s guest lectures, why company traces are becoming valuable assets, and Monaco’s unusual approach to brand building.

What’s the biggest bottleneck in your agent workflows: context, tools, or verification? Let us know in the comments.

Timestamps:

[00:00] Meet Mihail Eric

[00:16] Teaching AI Coding at Stanford

[02:42] Avoiding AI Slop

[04:48] Research, Plan, Build, Test

[06:19] Verifying Agent Work

[09:16] Lessons from Claude Code’s Creator

[12:40] Proactive AI Assistants

[16:13] Agent Memory & Context

[17:41] Rethinking Git for Agents

[19:32] AI Startup Pitches

[22:51] Why Traces Matter

[24:09] Better MCP Tools

[26:01] Monaco’s Sales Agents

[29:40] Turning Conversations into Insights

[33:33] AI Meets Human Expertise

[35:03] Poker & Brand Building

[36:46] Inside Monaco’s Agent Stack

[38:44] Designing Tools Around Intent

[40:12] Building an AI Advantage

[42:53] Wrapping Up