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three.dev
Find out how your AI agent fails. Before your users do.
215 followers
Find out how your AI agent fails. Before your users do.
215 followers
Almost every team now runs AI in production, but few know how it behaves once real users show up. The failures are quiet: a wrong refund policy, a made-up order status. Nothing crashes, so you hear about it from your users. three.dev finds those failures in your live traffic on its own and ranks them by severity. Setup is one prompt to Claude Code, Cursor, or Codex. Then your coding agent gets what it needs to write the fix.
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Verifying that your agent is doing what it’s supposed to be doing it’s still an unsolved problem. Exciting to see y’all tackle this!
I went through our website agent's chats and nothing looked broken. the miss was quieter: right answers, but it never offered the next step, so people just left. could three.dev flag a chat that's correct but goes nowhere?
Great question @olia_nemirovski!
The answer is yes, as long as the agent is instructed to do it. If the agent instructions say to offer a next step, three.dev is going to automatically detect and flag every chat where that doesn’t happen, regardless of whether the response is correct.
Additionally, we’re working on letting users record their business metrics (in your case, whether visitors take the next step or leave), so you can see how they change when you modify your prompt/model/configuration.
Detecting silent AI failures from real production traffic is valuable. How does three.dev distinguish genuine model mistakes from legitimate changes in business rules, and can developers control the severity thresholds for alerts?