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three.dev
Find out how your AI agent fails. Before your users do.
23 followers
Find out how your AI agent fails. Before your users do.
23 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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Hi Product Hunt, I'm Bryan, product lead at three.dev.
AI features can fail quietly. A support assistant tells a customer the refund window is 60 days when the policy says 30. Nothing crashes and no alert fires. You only find out when a customer complains, or when you happen to open the right request at the right time.
Most teams we've talked to have no way to see this. They read a few conversations by hand after a complaint. Evals don't close the gap, because you can only write an eval for a failure you already know about.
So three.dev starts from your live traffic:
Get started by pasting one setup prompt into your coding agent.
We score your live requests in the background, with no added latency.
Issues are found automatically and grouped into failure modes, ranked by severity, each with the real conversations and evidence behind it.
Hand the top one to your coding agent over MCP. It reads the examples and proposes a targeted fix.
I'd love to hear from anyone running AI in production:
How do you find out today when your AI gets something wrong?
What's the worst quiet failure you've caught, and how long did it run before you saw it?
For the Product Hunt community: sign up by Nov 22, send traffic within a week, and the Starter plan is free for six months. That's $474 off ($79 a month).
I'm here all day to answer questions. Try it on your own traffic and tell us what you find!
the refund window example (60 days when the policy says 30) is exactly the kind of failure nobody catches, no error no alert, just a confident wrong answer
how do you decide an answer is wrong without the source of truth??? do you pull in the docs whcih the agent used, or is it judged from the conversation alone???
@thamibenjelloun
Yeah, a wrong refund window is one of the many cases where the agent confidently answers something that is incorrect (and the user takes the answer for granted).
The evaluation is done on the conversation and the context that the agent retrieved when responding. For example, if the agent consulted the policy with a tool call, that additional context is given to the evaluator to determine whether the agent's response was acceptable or not.
In similar customer use cases, this allowed the evaluator to detect two big failure modes related to policies:
The agent did not consult the policy, but should have (based on the prompt instructions)
The agent consulted the policy, but it responded incorrectly