Agnost AI analyzes conversations between users and your production AI agents and discovers: silent failures, agent behavior drift, hallucinations, user frustration, hidden feature requests, and churn signals. It groups them into recurring patterns, shows the exact users and conversations behind each insight, and turns them into evals and fixes.
Hey Product Hunt! Shubham here 👋 Parth and I try almost every AI product we come across (we’re just young curious folks).
And we kept on doing the same thing: a product launched with an insane claim, their agent would feel magical for 10 minutes, then it claimed it completed something it hadn’t, invent a link, or make us repeat ourselves three times.
We’d then message the founders and hear: this is really useful feedback. we had no idea.
And we’d think: wait, you already have the entire conversation & traces. why did we have to tell you?
Turns out, their the AI observability dashboards showed a successful request: 200 OK, tool call succeeded, response generated.
The failure was only visible if someone actually read the conversation. So we built Agnost AI.
Agnost AI reads every production conversation across chat and voice agents. It groups them into recurring failures, behavior drift, hallucinated links, frustration, feature requests and churn signals, with the exact users and conversations behind each one.
From there, you can create an eval, or ask your coding agent to debug the problem & fix it.
Because evals test problems you already know about. You can’t write an eval for something you haven’t discovered yet.
Agnost AI connects in three lines of code or through OpenTelemetry and already analyzes more than one million messages every day.
If you’re running a user-facing agent, connect it. I’ll personally help you find three things happening in your conversations that you probably don’t know about.
Also, how do you currently discover failures your evals don’t cover: user complaints, manually reading traces, or something else?
It claimed it finished something it had not is the exact failure that reaches me as a support ticket, never as a failed test. Do you catch the confident wrong answer, or only the ones where the agent visibly gives up?
Congrats on the launch team!!! I played around with the product and it felt super snappy
You two kept messaging founders about failures in their agents and kept hearing that they had no idea. Congrats on shipping the version where they find out first, without needing you to tell them.
You noticed that an invented link still comes back as a 200 OK on the dashboard. Congrats on building the thing that actually reads the conversation.
Can I also save my model inference costs using the insights Agnost gives me?
About Agnost AI on Product Hunt
“Catch agent failures your evals miss”
Agnost AI launched on Product Hunt on August 25th, 2026 and earned 277 upvotes and 18 comments, earning #3 Product of the Day. Agnost AI analyzes conversations between users and your production AI agents and discovers: silent failures, agent behavior drift, hallucinations, user frustration, hidden feature requests, and churn signals. It groups them into recurring patterns, shows the exact users and conversations behind each insight, and turns them into evals and fixes.
Agnost AI was featured in Analytics (173.4k followers), Developer Tools (518.1k followers) and Artificial Intelligence (476.9k followers) on Product Hunt. Together, these topics include over 213.4k products, making this a competitive space to launch in.
Who hunted Agnost AI?
Agnost AI was hunted by Garry Tan. 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 Agnost AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Garry Tan
Hey Product Hunt! Shubham here 👋
Parth and I try almost every AI product we come across (we’re just young curious folks).
And we kept on doing the same thing: a product launched with an insane claim, their agent would feel magical for 10 minutes, then it claimed it completed something it hadn’t, invent a link, or make us repeat ourselves three times.
We’d then message the founders and hear: this is really useful feedback. we had no idea.
And we’d think: wait, you already have the entire conversation & traces. why did we have to tell you?
Turns out, their the AI observability dashboards showed a successful request: 200 OK, tool call succeeded, response generated.
The failure was only visible if someone actually read the conversation. So we built Agnost AI.
Agnost AI reads every production conversation across chat and voice agents. It groups them into recurring failures, behavior drift, hallucinated links, frustration, feature requests and churn signals, with the exact users and conversations behind each one.
From there, you can create an eval, or ask your coding agent to debug the problem & fix it.
Because evals test problems you already know about. You can’t write an eval for something you haven’t discovered yet.
Agnost AI connects in three lines of code or through OpenTelemetry and already analyzes more than one million messages every day.
If you’re running a user-facing agent, connect it. I’ll personally help you find three things happening in your conversations that you probably don’t know about.
Also, how do you currently discover failures your evals don’t cover: user complaints, manually reading traces, or something else?