Reflexio captures every user correction, failure, or success, turning them into testable learning rules that the agent applies automatically. Over time, this reduces task failures by over 30% and cuts token usage by more than 60%.
Reflexio works best with agents that expose interaction logs and allow rule injection. While it can be adapted to many frameworks, legacy or closed‑source agents may need custom adapters to fully leverage Reflexio’s learning engine.
Reflexio records the context, outcome, and token cost of each interaction, then statistically evaluates which rules improve success rates. Only evidence-backed rules are promoted, ensuring that every learning is both testable and reversible.