
three.dev
Find recurring failures in AI agent conversations and compare behavior across releases.
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About this product
three.dev helps teams see how an AI agent behaves after deployment. It routes model calls through its proxy, scores a configurable sample, and groups problems into recurring failure modes. Each issue points back to the conversation evidence. A coding agent can retrieve that context through MCP, and the team can compare two versions using the same evaluation criteria.
Start with synthetic or redacted test traffic and define one failure you can judge by hand. For a support agent, that could be quoting the wrong return window. Compare failures as a share of scored requests, especially when comparing different traffic volumes or sampling rates. Check what conversation data the proxy records before connecting production traffic.
The site offers early access. Its pricing page lists 5,000 scored requests and 1 GB of processed data per month on the free plan, with Starter listed at $79 per month.
Features
- Conversation scoring and issue evidence
- Grouped failure modes
- Coding agent access through MCP
- Before and after traffic comparisons
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