Learning platform that turns an AI agent's real conversations, corrections and failures into visible, revocable behavior changes it reuses.

Reflexio works by combining Self-Improvement Loop: Every conversation the agent has feeds back in, so repeated failures become a learning the agent reuses rather than a mistake it makes again next week., Learning Retirement on Contradiction: When newer conversations contradict an existing learning, the old one is retired automatically, keeping the agent aligned with current policy and product reality., Self-Tuning Learnings: Reflexio watches how each learning performs in production — the sessions it improved and the ones it did not — and revises it from that evidence through a continuous optimization process., Visible and Revocable Behavior: Each learning is a readable artifact you can inspect and revoke, so behavior changes are auditable instead of an opaque model update., Portable Integration Skill: A published SKILL.md that Codex, Claude Code or Cursor can follow to inspect your agent's lifecycle, implement the retrieve-and-publish loop and verify the changed path. to help users with Stopping Repeated Support Failures: Turn a recurring miss — like resolving one charge when the user had two — into a learning that makes the agent check the full window before answering., Keeping Agents Current with Policy Changes: Let a changed refund window or product rule propagate into agent behavior automatically as newer conversations contradict the old learning., Mining Existing Logs for Improvements: Extract behavior fixes from conversation history you already have instead of hand-writing ever-longer system prompts., Improving a Coding Agent Over Time: Feed a coding agent's successes and failed paths back in so it stops repeating the same wrong approaches on your codebase., Auditing Agent Behavior Changes: Review and revoke individual learnings when a compliance or quality reviewer needs to know exactly why an agent's behavior changed..

Key features include Self-Improvement Loop: Every conversation the agent has feeds back in, so repeated failures become a learning the agent reuses rather than a mistake it makes again next week., Learning Retirement on Contradiction: When newer conversations contradict an existing learning, the old one is retired automatically, keeping the agent aligned with current policy and product reality., Self-Tuning Learnings: Reflexio watches how each learning performs in production — the sessions it improved and the ones it did not — and revises it from that evidence through a continuous optimization process., Visible and Revocable Behavior: Each learning is a readable artifact you can inspect and revoke, so behavior changes are auditable instead of an opaque model update., Portable Integration Skill: A published SKILL.md that Codex, Claude Code or Cursor can follow to inspect your agent's lifecycle, implement the retrieve-and-publish loop and verify the changed path..

Reflexio is useful for anyone interested in Stopping Repeated Support Failures: Turn a recurring miss — like resolving one charge when the user had two — into a learning that makes the agent check the full window before answering., Keeping Agents Current with Policy Changes: Let a changed refund window or product rule propagate into agent behavior automatically as newer conversations contradict the old learning., Mining Existing Logs for Improvements: Extract behavior fixes from conversation history you already have instead of hand-writing ever-longer system prompts., Improving a Coding Agent Over Time: Feed a coding agent's successes and failed paths back in so it stops repeating the same wrong approaches on your codebase., Auditing Agent Behavior Changes: Review and revoke individual learnings when a compliance or quality reviewer needs to know exactly why an agent's behavior changed..

Reflexio offers a free tier with paid plans for advanced features.

Visit https://www.reflexio.ai/ to sign up and explore Reflexio.