Description

Glass is a Quality Control layer for AI systems that turns anomalies into automated improvements.

Today’s AI agents fail in subtle ways: hallucinations, broken workflows, unsafe outputs, or silent reasoning errors. These failures are difficult to detect systematically and even harder to translate into meaningful improvements. As a result, teams rely on manual debugging, scattered logs, and slow iteration cycles.

Glass closes that loop.

It continuously monitors AI behavior in production, detects anomalies and misbehaviors, and automatically converts them into structured evaluation cases. Instead of letting failures disappear into logs, Glass turns them into signals that feed a continuous evaluation pipeline.

Every detected issue becomes a test. Every test becomes feedback. Every feedback cycle improves the system.

Over time, this creates an infinite feedback loop where real-world failures automatically generate the evaluations needed to refine prompts, guardrails, and agent architectures.

Glass sits between your AI agents and the real world, acting as a quality layer that ensures systems get better the more they are used.

Rather than reacting to AI failures after they affect more users, Glass transforms them into the engine that drives continuous improvement.

The result is faster iteration, safer deployments, and AI systems that evolve in real time.