Education researchers have spent a century figuring out how to build environments where complex, unpredictable systems self-correct through structured feedback. Software engineers building AI agents are solving the exact same problem from scratch and ignoring all of it. This talk bridges that gap. I'm a former classroom teacher turned AI engineer, and I'll walk you through five pedagogical frameworks that map directly to eval design patterns for AI agents: backward design (define success criteria before you build), formative assessment (eval continuously, not just at the end), rubric design (multi-dimensional scoring instead of pass/fail), error analysis (categorize failure modes because same symptom doesn't mean same cause), and differentiated feedback (the agent, the user, and the knowledge base each need their own signal channel). What ties them together is back pressure: each framework is a way to capture signal from problems and route it to where it drives change. That's what makes a system self-correcting instead of just self-reporting. Most AI observability is still about watching systems after the fact. This talk is about designing systems where the eval layer captures back pressure from failures and feeds it back upstream, so the system iterates on itself. I've built production agents and won hackathons with this approach, and the core insight is simple: the best eval systems aren't tests, they're environments. And nobody knows more about designing those environments than teachers.
