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?
About Agnost AI on Product Hunt
“Catch agent failures your evals miss”
Agnost AI launched on Product Hunt on August 25th, 2026 and earned 265 upvotes and 17 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.
On the analytics side, Agnost AI competes within Analytics, Developer Tools and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Agnost AI performed against the three products that launched closest to it on the same day.
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.
For a complete overview of Agnost AI including community comment highlights and product details, visit the product overview.
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?