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.

Humans used to give infrastructure a breather. AI agents don’t. In this episode of Debug Mode, Erika and Demetrios explore what happens when one person’s prompt triggers a burst of searches, API calls, and compute-intensive requests - and why existing services, gateways, and pricing models can struggle to keep up.

Two people, one tandem bike, and a lot of near misses along Amsterdam’s canals. Dexter Horthy, CEO and co-founder of HumanLayer and the person who coined “context engineering,” climbs on the back to talk about a problem every AI-heavy engineering team eventually hits: code that runs today that nobody understands three months later. The ride starts with an old joke from the Claude 3.7 days: ship the slop, it’ll be GPT 7’s problem. That joke is aging badly now that GPT 6 is out and still can’t design clean abstractions. From there it becomes a tour of how agents are actually used right now. Computer use checks in for flights and books government appointments. Two hours of raw footage became a rough but promising product demo through Opus 4.8 and the Palmier MCP while Dex was out for a run. And the best MCP tools turn out to be the ones that mirror what Claude Code and Codex were trained to call.