Reflexio makes AI agents better with every interaction. When users correct an agent, a path fails, or something works particularly well, Reflexio turns that experience into behavior the agent can reuse next time. Instead of leaving valuable lessons buried in logs, your agent continuously learns what to repeat and what to avoid — with every learning visible, testable, and reversible. Reduce task failure rate by more than 30%, while saving tokens by more than 60%.
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.
Your agent can remember every correction and still repeat the same mistake.
Memory preserves what happened. Learning turns the outcome into a reusable rule for what the agent should do differently next time.
That s how Reflexio enables knowledge sharing: a lesson from one interaction can become a tested, scoped, and reversible behavioral improvement that benefits every user not just the person who provided the correction.
The real measure of learning isn t whether an agent recalls its mistakes. It s whether those mistakes become less common over time.
