[AMA] Closing the Loop: What Does a Production Eval Flywheel Look Like?

​Description

​Shipping an agent is only the beginning. The real challenge is building a system that helps you continuously learn from production behavior, improve quality, and prevent regressions over time.

​In this session, we’ll walk through what a production eval flywheel actually looks like in practice. We’ll cover how teams move from real-world traces and user interactions to identifying failure patterns, creating structured datasets, running evaluations, and feeding those learnings back into prompts, tools, and application logic. The goal is to make evaluation an ongoing part of your development lifecycle, not a one-off exercise.

​This session is designed for builders and technical teams who want a clearer framework for turning production data into better agent performance. We’ll share practical guidance, common patterns, and where teams often get stuck when trying to operationalize evals in real environments.

​Join us for a 30-minute deep dive followed by live Q&A, where we’ll answer your questions and discuss how to build a repeatable loop for improving agents in production.