Fabraix - Find gaps in your AI agents before users do
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AI agents fail in ways traditional software doesn't. Our agents help you find all the ways in which your AI agents fail by adversarially testing them in a dedicated environment. Point it at any AI agent, or multi-agent system, and it launches 1,000+ strategies that adapt to your system in real time - pure blackbox, no integration needed.
Built by ex-Meta engineers.
Replies
I've been going through a lot of AI agent launches this week and the thing nobody seems to talk about is what happens when they quietly fail. Most products just show you the best case. What got me about Fabraix is that it's the first thing I've seen that's specifically built to find the worst case before your users do. My question is more basic though ,when Nyx finds something, how does it explain it to someone who isn't an engineer? Like does the finding come with "here's what went wrong and here's why it matters" or is it a technical report that only a developer can read?
Adversarial testing for agents is the right framing because the failure surface looks nothing like traditional software. At ~120 engineers I struggled to even get my team to agree on what counted as "agent broken" versus "agent making a reasonable judgment call I disagree with." The 1,000+ strategies pitch is interesting, but I'd want to see the false-positive shape early - if the noise floor is high, the team stops reading the reports within two sprints. What does signal-to-noise look like once teams have been running Fabraix for a few weeks?