Reflexio is a learning platform designed to transform AI agents into self-improving systems by capturing, validating, and applying user feedback to agent logic.
What the product does and how it is positioned
Reflexio functions as a behavioral learning layer that sits alongside AI agents to capture lessons from real-world interactions. Instead of relying on static memory, the platform continuously updates agent logic based on user corrections and successful outcomes.
The system provides developers with granular control over agent evolution. By scoring new learnings against performance metrics and allowing for manual review or rejection, Reflexio is designed so that agents adapt to changing product requirements without retaining outdated or incorrect behaviors.
Source-supported ways to use the product
Agents learn to identify and resolve multiple user issues, such as recognizing multiple unrecognized charges in a single interaction rather than addressing them sequentially.
Coding agents utilize Reflexio to extract actionable feedback from syntax errors or tool usage corrections, improving accuracy in future development tasks.
The documented workflow, where available
The agent application publishes interaction data, including user feedback and outcomes, to the Reflexio platform.
Reflexio extracts actionable signals, resolves conflicts, and tunes the learning based on performance evidence.
The agent retrieves relevant, validated learnings at the moment of inference to inform its next response.
Reflexio operates through a continuous loop that prioritizes evidence-based improvement over static instruction. When an agent encounters a scenario, the platform evaluates the outcome against defined success metrics. If a correction is provided, the system captures the trigger and the desired behavior, storing it as a learning.
to help reduce behavioral drift, the platform employs a background process that de-duplicates and resolves conflicts between new and existing learnings. Older learnings are retired when newer interactions provide contradictory evidence, supporting the agent remains aligned with current product policies.
Checks to run with your own material and workflow
What was checked and when
Answers based on the source-checked product record
Traditional memory layers typically store raw user input or facts for retrieval. Reflexio focuses on behavioral learning, capturing how an agent should act differently based on past corrections and outcomes.
Yes, every learning is auditable and under your control. You can manually approve, reject, or delete any learning, and a rejected learning is immediately removed from the agent's retrieval process.
No, Reflexio does not retrain the model. It functions as a persistent context and learning layer that injects relevant, actionable signals into the agent's workflow at the moment of inference.
Reflexio supports multiple deployment models, including fully managed services, Bring Your Own Key (BYOK), Bring Your Own Cloud (BYOC), and fully self-hosted, air-gapped installations.
Reflexio uses a background process to de-duplicate and resolve conflicting signals. This is designed so that the agent does not receive contradictory instructions and is intended to reduce behavioral drift over time.
