> ## Documentation Index
> Fetch the complete documentation index at: https://glassray.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Welcome to Glassray

> The evaluation layer that makes your AI agents self-improve - learn what your agents are supposed to do, catch recurring deviations in production, and propose code-level fixes.

<Note>
  Glassray is in **private alpha**. [Join the alpha](https://glassray.ai/waitlist) and we'll connect your traces, walk through your agent's code, and get the loop running on your production data.
</Note>

## What is Glassray?

Glassray is the evaluation layer that makes your AI agents self-improve. It connects to the traces your agents already emit, learns what each part of your system is *supposed* to do, then scans your production traffic for recurring misbehavior and proposes code-level fixes.

Most evaluation tooling scores final outputs. Glassray works from a model of **intent**: it reads your agent's code to learn what each step should do, generates a plain-language spec, and checks production traces against it. That's how it surfaces silent failures - answers that look right but were produced the wrong way.

<CardGroup cols={2}>
  <Card title="Learns your system" icon="book-open">
    Reads your traces and your code to map your agents into flows and generate a spec of intended behavior.
  </Card>

  <Card title="Finds recurring deviations" icon="ghost">
    Clusters production traces into recurring types of misbehavior, not just one-off errors.
  </Card>

  <Card title="Proposes code-level fixes" icon="wand-sparkles">
    On an accepted deviation, suggests a prompt or logic change as a diff, with the failing trace attached.
  </Card>

  <Card title="Scans for more instances" icon="magnifying-glass">
    Runs a deep search across your trace history to find every other place the same deviation shows up.
  </Card>
</CardGroup>

## Who it's for

Glassray is built for teams running LLM agents in production: multi-step pipelines, tool-calling agents, retrieval chains, and multi-agent systems. If your agent's traces land in a tracing system like Langfuse, LangSmith, or PostHog - or you can push OpenTelemetry - Glassray can work with them.

## Next steps

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Connect your data and run the loop, step by step.
  </Card>

  <Card title="Flows" icon="diagram-project" href="/flows">
    How Glassray groups your traces into flows - the unit it tracks quality on.
  </Card>

  <Card title="Connect over MCP" icon="plug" href="/mcp-server">
    Let your coding agent query your org's traces, flows, and deviations.
  </Card>

  <Card title="Join the alpha" icon="circle-check" href="https://glassray.ai/waitlist">
    Get access and we'll get the loop running on your production data.
  </Card>
</CardGroup>