Operations | Monitoring | ITSM | DevOps | Cloud

If 90% of code is now AI-generated, what happens to the engineers who never had to write it themselves? The ability to review code well, to catch what the LLM missed, depends on mental models that only come from practice. That tension sits at the center of this conversation.

AI coding agents write code faster than your pipeline can catch mistakes. What if the agent could validate against CI before you ever push? In this 5-minute demo, we set up CircleCI's Chunk CLI and run a microbuild using Chunk sidecars, secure Linux microVMs that spin up in ~1 second in your CircleCI account, mirror your working directory (no git push required), and give your agent CI-grade feedback while it's still in context.

Cloud killed the overprovisioned server rack. Agents might do the same thing for engineering teams.

Your engineers don't suddenly become less valuable because they hit a token cap.

Most teams obsessing over token costs are measuring the wrong thing. The real savings from AI aren't in lines of code written faster. They're in the coordination overhead that disappears when fewer humans need to align before anything gets built. Chris Kelly, Head of Product at Augment Code, joins Rob to cover why prototypes have replaced specs, how agents enable dynamic team capacity the way cloud replaced over-provisioned servers, and what "good code" even means when your primary reader is an LLM. In this episode.