Agents That Self Improve While You Sleep - Fuad Ali, Arize AI
Arize AI's Signals platform creates autonomous self-improvement loops for AI agents using traces and feedback
“how do you actually start building a loop where these agents use your feedback as a human, customer reviews, and of course real evidence like traces to know how they can improve”
Arize AI product manager Fuad Ali presented a workshop on building autonomous self-improvement cycles for AI agents using Arize's Signals platform, which ingests traces, human feedback, and customer reviews to iteratively improve agent behavior. The talk addresses a core production challenge: non-deterministic agents at scale are difficult to monitor and improve systematically. Signals offers a managed platform combining datasets, evaluations, and experiments into a continuous improvement loop.