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.
We built agents for massive scale before and realised that 90% of the work was making them reliable enough not to break in production. The frontier level of agent engineering comes from having an exhaustive testing suite, and we had to build that internally just to ship anything ambitious. So we're building it for everyone else.
Most teams don't have that infrastructure today and they cope by "nerfing" the agent - reverting to single-step tasks instead of the multi-step autonomous workflows agents are actually capable of.
Our agent is an offensive AI that stress-tests your AI agents. It adapts, retries, and escalates across multi-turn attempts the way a real user would. Pure blackbox, no integration. Point it at any agent and let it run.
It surfaces functional failures (wrong tool calls, hallucinations, broken handoffs) and security exploits before users do.
What we can help with: Confidence that the agents you've already deployed hold up against the failure modes that matter. Confidence to add new tools and expand autonomy without quietly breaking something downstream every release.
Built by a team of ex-Meta and Monzo engineers. We'd genuinely love feedback from anyone who's been facing an issue with testing AI agents.
Fabraix is an essential product in any agent-builder’s toolkit👀
As the whole world focuses on building agents for various use-cases, testing is the last-mile gap that is still unaddressed. From creating 1000s of prompts to “test” to not knowing whether outputs would work reliably and even worse: not knowing how changing models would affect your agents, agent testing is broken.
Fabraix offers a refreshing, automated and fuss-free agentic QA that makes sure your agents always runs, reliably.
All the best with this launch @zachx0 and @ibrahim_abdu1 🚀🚀
Arx adds runtime action checking (/check) alongside event logging (/event): how do you recommend teams decide what to gate synchronously vs only observe, and what have you learned about keeping false positives and latency low while still blocking real prompt-injection/goal-deviation attempts?
Hey Product Hunt 👋
Just to add to what Zach said, we really believe agentic reliability is the biggest hurdle to overcome before we can really realise the productivity benefits of agents, and it's starts with being able to evaluate them. How can you build something reliable, if you don't know where it fails?
Would love feedback and comments on our approach!
About Fabraix on Product Hunt
“Find gaps in your AI agents before users do”
Fabraix launched on Product Hunt on May 8th, 2026 and earned 227 upvotes and 18 comments, placing #6 on the daily leaderboard. 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.
Fabraix was featured in Developer Tools (512.4k followers), Artificial Intelligence (468.5k followers) and YC Application (46 followers) on Product Hunt. Together, these topics include over 162.2k products, making this a competitive space to launch in.
Who hunted Fabraix?
Fabraix was hunted by Sharath Kuruganty. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Fabraix stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Sharath Kuruganty
Hey Product Hunt 👋
We built agents for massive scale before and realised that 90% of the work was making them reliable enough not to break in production. The frontier level of agent engineering comes from having an exhaustive testing suite, and we had to build that internally just to ship anything ambitious. So we're building it for everyone else.
Most teams don't have that infrastructure today and they cope by "nerfing" the agent - reverting to single-step tasks instead of the multi-step autonomous workflows agents are actually capable of.
Our agent is an offensive AI that stress-tests your AI agents. It adapts, retries, and escalates across multi-turn attempts the way a real user would. Pure blackbox, no integration. Point it at any agent and let it run.
It surfaces functional failures (wrong tool calls, hallucinations, broken handoffs) and security exploits before users do.
What we can help with: Confidence that the agents you've already deployed hold up against the failure modes that matter. Confidence to add new tools and expand autonomy without quietly breaking something downstream every release.
Built by a team of ex-Meta and Monzo engineers. We'd genuinely love feedback from anyone who's been facing an issue with testing AI agents.