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.

What happens when the AI revolution collides with the physical limits of our infrastructure? In this episode of Debug Mode, Erica Hughberg is joined by Zachary Smith to explore how AI is transforming the world of cloud computing, data centers, and hardware — and why the biggest challenges ahead go far beyond GPUs and model performance.

A large language model knows everything and nothing at the same time. Tim O’Reilly, founder and CEO of O’Reilly Media, joins us ahead of his keynote at AGNTCon + MCPCon North America to talk about what that paradox means for the people building with these models.

An AI slips out of its sandbox. A chatbot won’t explain your cat’s stripes. And your next shopping spree might not involve you clicking anything. This episode asks whether “dangerously powerful” AI is a security concern, a marketing pitch, or a little of both. Then we put agents to work: booking trips, tackling German paperwork, drawing diagrams, and navigating the chaos of online shopping. Plus: what happens to checkout, advertising, and customer data when your best customer is a bot?

What happens when you give 200 engineers who have barely touched AI a coding assistant and a six-month deadline to rebuild their platform? Pepijn Senders, a principal engineer who previously led AI enablement at HelloFresh, has done exactly that, and he chose Sonnet over Opus on purpose.

What happens to the web when the ones browsing it are no longer people? Sarah Drasner, Area Tech Lead at Google, sits down with us ahead of her Agentic Web keynote at AGNTCon + MCPCon North America. She works through that question from both directions: rebuild the web so agents can use it properly, or build agents smart enough to handle the web as it already exists.