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Tessary
Stop your agents from failing silently in production
Tessary is an open-source agent reliability platform. It monitors every production trace, detects failures that sampled evals miss, and investigates the root cause with your agent codebase. It runs cheap, narrow classifiers across every trace. When it detects a recurring issue or change in behaviour, it groups the affected traces into a finding, validates the issue, and investigates what caused it. This keeps monitoring cheap, with deeper analysis reserved for issues that warrant investigation.
About a year ago, @nina_nixes and I started building synthetic users for usability testing of B2B software. We built agents that would personify real users and use the software, but we couldn't get them to work reliably enough over long sessions.
We built a massive eval set to tune them, but then evaluating a single agent session became about 5x more expensive than actually running it because we needed to grade across its many narrow intents.
That's where Tessary originated. We wanted to catch more of these failures and understand what caused them without making evaluation prohibitively expensive.
The approach we've built uses cheap, narrow classifiers to monitor every production trace, with deeper analysis reserved for issues that warrant investigation. It's open source, and you can self-host it or try it on our free Cloud plan. Here's a quick interactive walkthrough to check it out - Demo.
I'd love to hear from others working on agent reliability. What failures are you still catching manually, and what have you had to build your own checks for?
Thanks for taking a look. @nina_nixes and I are here for questions and feedback.
About Tessary on Product Hunt
“Stop your agents from failing silently in production”
Tessary was submitted on Product Hunt and earned 23 upvotes and 9 comments, placing #31 on the daily leaderboard. Tessary is an open-source agent reliability platform. It monitors every production trace, detects failures that sampled evals miss, and investigates the root cause with your agent codebase. It runs cheap, narrow classifiers across every trace. When it detects a recurring issue or change in behaviour, it groups the affected traces into a finding, validates the issue, and investigates what caused it. This keeps monitoring cheap, with deeper analysis reserved for issues that warrant investigation.
On the analytics side, Tessary competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Tessary performed against the three products that launched closest to it on the same day.
Who hunted Tessary?
Tessary was hunted by Akhil Varma. 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.
For a complete overview of Tessary including community comment highlights and product details, visit the product overview.
Akhil Varma
Hey PH 👋, I'm Akhil, co-founder of Tessary.
About a year ago, @nina_nixes and I started building synthetic users for usability testing of B2B software. We built agents that would personify real users and use the software, but we couldn't get them to work reliably enough over long sessions.
We built a massive eval set to tune them, but then evaluating a single agent session became about 5x more expensive than actually running it because we needed to grade across its many narrow intents.
That's where Tessary originated. We wanted to catch more of these failures and understand what caused them without making evaluation prohibitively expensive.
The approach we've built uses cheap, narrow classifiers to monitor every production trace, with deeper analysis reserved for issues that warrant investigation. It's open source, and you can self-host it or try it on our free Cloud plan. Here's a quick interactive walkthrough to check it out - Demo.
I'd love to hear from others working on agent reliability. What failures are you still catching manually, and what have you had to build your own checks for?
Thanks for taking a look. @nina_nixes and I are here for questions and feedback.