Our journey to Reflexio: teaching agents to learn from experience

The journey of building Reflexio started from a painful lesson Yi and I learned firsthand.

At our previous company, we worked on the personalization service and memory infrastructure powering AI agents at very large scale, and saw how hard it is to make agents actually learn from experience. It wasn’t just about storing user facts. Teams spent huge amounts of time reviewing production conversations: where agents failed, where users corrected them, which tools were called incorrectly, and how those lessons could be turned into better behavior through metrics, evaluations, and experiments.

That kind of learning infrastructure is powerful, but it takes serious engineering investment — the sort only a handful of companies can afford. Most agent builders and startups don’t have a dedicated platform team of that size behind them.

That became our “aha” moment: as AI agents become more useful, every team will need a way for agents to learn continuously from real interactions, changing environments, and user feedback.

Earlier this year, we left to build Reflexio: a learning platform for AI agents. We prototyped it locally, offered it as a cloud service, integrated it with coding agents like Claude Code and Codex, and worked closely with design partners to refine the product through real usage.

Today, we’re excited to launch Reflexio on Product Hunt. It’s already being tested with design partners and enterprise customers, but this is just the beginning. If you’re building AI agents and believe they should get better every time they interact with the world, we’d love for you to try Reflexio, share feedback, and join us on the journey.