Agents That Self Improve While You Sleep - Fuad Ali, Arize AI
In this workshop, Fuad Ali of Arize AI shows how to build a self-improvement loop for AI agents: trace everything, let Signal (an always-on agent) read your traces on a schedule and file deduplicated issues, turn failures into evals and living regression datasets, run experiments to prove a fix works, and keep humans at the highest-leverage points: eval design, dataset curation and reviewing fixes.
He demos the loop on a toy-shopping agent with a planted bug and takes questions on evaluating qualitative output, decision models and cost. Chapters 00:00 Intro and workshop setup 05:42 Where is your team on the maturity curve?
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