Reflexio is a behavioral learning platform that enables AI agents to improve by extracting actionable rules from real user interactions, corrections, and outcomes. It captures lessons learned during agent runs and makes them retrievable for future use, without retraining models. The platform offers a portable skill for coding agents, plus Python, REST, and CLI integrations, and supports deployment from fully managed to self-hosted environments.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents often repeat the same mistakes because they lack a mechanism to learn from past interactions, user corrections, or failed paths. Traditional memory systems store facts that may or may not be retrieved, offering no way to verify whether a memory actually improved behavior or to undo a bad one. This results in static agents that fail to self-improve, leading to repeated errors and user frustration.
Solution
Reflexio provides a behavioral learning platform that turns user corrections, failed paths, and successful outcomes into actionable rules that agents reuse. The platform captures the correction and its trigger together, allowing agents to retrieve and apply these rules in future runs without retraining. Each learning is tuned by the evidence it produces, scored against the un-augmented response, and can be approved, rejected, or retired by the user. Reflexio integrates via a portable skill for coding agents or through Python, REST, or CLI, and runs anywhere from fully managed to fully self-hosted.
Target Audience
Primary customers are engineering and AI teams building production-grade AI agents who need a systematic way to capture, apply, and audit behavioral improvements without retraining models.
Features
- Captures corrections and triggers together, creating rules that are readable and stored in a user-controlled queue
- Scores responses against the un-augmented baseline to measure whether a learning actually helped
- Supports approval, rejection, or retirement of learnings, with rejected rules dropping out of retrieval
- Provides a portable skill for coding agents like Codex, Claude Code, or Cursor, plus Python, REST, and CLI integration options
- Offers deployment flexibility from fully managed (with isolated schema) to fully self-hosted, with an unchanged API
- Includes self-tuning learnings that are revised based on evidence produced, with version history (v1, v2, v3)