Hi Product Hunt — I’m Matt, Co-Founder & CEO of Okareo 👋
Thrilled to launch Okareo Error Reporting today 🚀
If you’re spending hours chasing down Agent or RAG issues from scattered traces, Okareo can help. We deliver real-time error reporting through behavioral alerts, seamlessly connected to a structured evaluation and persona-based simulation suite — so you can debug more conditions, faster, and with confidence.
Our immediate goal is to help teams ship agents to production faster and with higher confidence — but the bigger vision is a virtuous loop where agents continuously self-improve.
We’d love for you to take it for a spin and share your feedback — what’s working, what’s missing, and what you'd love to see next.
@matt_wyman Hey Matt, Interesting launch. Congrats. This seems like a big issue. These Agents eat up resources when they end up in loops erroneously. Do you have any numbers to share of simple AI agents like a AI calling app as a common use case.
Hello @imraju ! I'm an ML engineer at Okareo, and I can give some insight here.
An agent looping is indeed a common and highly wasteful error pattern. On our error detection platform, we have a "check" (i.e., an LLM-based evaluation) called "Loop Guard." Loop Guard helps us detect when agents are stuck in repetitive patterns, and for one of our development partners, we have seen as much as 25% of their production traffic shows looping behavior.
Hello there @halgod 👋🏽 when we apply a "check" (i.e., an LLM-based evaluation) to an incoming datapoint, the check returns both an outcome (i.e., "pass" or "fail") as well as an explanation. The explanation can be used to help identify the root cause of a failure and to inform the agent developer what improvements can be made to the agent (or the agent network).
Hey, nice launch! It's important to have visibility into what's happening with the complex systems of LLMs. How does this handle false positives/false negatives?
Great question, @pranay12 — false positives/negatives are a big deal, especially for alerts.
Our built-in checks are tuned on large datasets to reduce noise. When writing your own, you can generate synthetic scenarios and collect structured feedback to fine-tune them really quickly.
Amazing launch, can think of tons of ways this tech could be applied. Especially as tool call chains become more complex, each execution is a surface area for errors.
Replies
Hi Product Hunt — I’m Matt, Co-Founder & CEO of Okareo 👋
Thrilled to launch Okareo Error Reporting today 🚀
If you’re spending hours chasing down Agent or RAG issues from scattered traces, Okareo can help. We deliver real-time error reporting through behavioral alerts, seamlessly connected to a structured evaluation and persona-based simulation suite — so you can debug more conditions, faster, and with confidence.
Our immediate goal is to help teams ship agents to production faster and with higher confidence — but the bigger vision is a virtuous loop where agents continuously self-improve.
We’d love for you to take it for a spin and share your feedback — what’s working, what’s missing, and what you'd love to see next.
Thanks for checking us out!
@matt_wyman Hey Matt, Interesting launch. Congrats. This seems like a big issue. These Agents eat up resources when they end up in loops erroneously. Do you have any numbers to share of simple AI agents like a AI calling app as a common use case.
Hello @imraju ! I'm an ML engineer at Okareo, and I can give some insight here.
An agent looping is indeed a common and highly wasteful error pattern. On our error detection platform, we have a "check" (i.e., an LLM-based evaluation) called "Loop Guard." Loop Guard helps us detect when agents are stuck in repetitive patterns, and for one of our development partners, we have seen as much as 25% of their production traffic shows looping behavior.
@matt_wyman Nice launch Matt. Agents self evolve is key, how do you explain to the users though? How exactly do we know it can improve?
@matt_wyman BTW upvote to you!
Hello there @halgod 👋🏽 when we apply a "check" (i.e., an LLM-based evaluation) to an incoming datapoint, the check returns both an outcome (i.e., "pass" or "fail") as well as an explanation. The explanation can be used to help identify the root cause of a failure and to inform the agent developer what improvements can be made to the agent (or the agent network).
Hey, nice launch! It's important to have visibility into what's happening with the complex systems of LLMs. How does this handle false positives/false negatives?
Great question, @pranay12 — false positives/negatives are a big deal, especially for alerts.
Our built-in checks are tuned on large datasets to reduce noise. When writing your own, you can generate synthetic scenarios and collect structured feedback to fine-tune them really quickly.
No more sifting through a mess of traces – debugging just got a whole lot clearer (and faster!). 🆒
Okareo is phenomenal, was one of their first customers and they absolutely crushed it
Real-time monitoring is a must for AI! 👍
Fewsats
Few teams in this space understand what needs to be built to solve LLM observability and reporting challenges as effectively as Okareo does.
Congratulations on the launch! 🚀
Productroadmap.ai (by Ignition)
Congrats on the launch! Love using Okareo!
ion design
Amazing launch, can think of tons of ways this tech could be applied. Especially as tool call chains become more complex, each execution is a surface area for errors.
I like the UI !
Is it handcoded or inspired by AI ?
Hello @sum ! We use AI to help us out here and there, but our app is fundamentally designed and written by humans :)
Super useful for anyone building with LLMs! ⚠️ Real-time behavioral alerts and structured debugging are a game-changer for agent and RAG reliability.