The Agentic Lab Factory: How I Stopped Writing Labs and Started Reviewing Them
Time and room for this session are published in October. when the timetable goes live.
For years, getting new contributor SMEs to write labs was an uphill battle. The learning curve was too steep, the standards too specific, and the creation cost too high. What would it take to change that equation with AI?
This is the story of how I built an agentic lab creation pipeline using Claude: starting by analyzing 40 existing labs to make tacit knowledge explicit, generating our own style guidelines, and feeding those back as the generative substrate. The result is a loop that implements, tests, scores pedagogy, and incorporates reviewer feedback autonomously, only stopping when every gate passes.
The key insight: AI is a catalyst, not a creator. It amplified what was already good, our labs, our standards, our criteria. Without that foundation, the output would have been slop. With it, the bottleneck dissolved.
New contributors who previously found the learning curve too steep can now prompt their copilot agent of choice and produce a lab that meets our standards, complete with pregenerated Credly badge templates. You'll leave with a framework for thinking about agentic workflows in terms of deterministic vs. non-deterministic gates, and a concrete example of what happens when the loop mostly runs itself.
