Agnost AI: Catching Agent Failures Your Evals Miss in Real Conversations
Launch HN: Agnost AI (YC S26) – Extract user feedback from agent conversations
I built Agnost AI to solve a critical gap where your evaluations pass but production agents still fail. Our platform analyzes real user conversations to surface missed failures, extract feature requests, and even generate pull requests for fixes automatically. Backed by Y Combinator, we help teams like Google and Exa turn hidden feedback into actionable improvements, ensuring your agents get smarter faster without manual review.
Agnost AI showed me users were asking for features we don't even have. I had no idea. All that feedback was just sitting inside conversations we already had.
- kianN
I see a fair number of comments here advocating for either codex to hand-roll this themselves, or to simply punt to SQL. I do want to advocate for the difficulty of the problem, even if I can't speak to the company itself.
At the scale of a few hundred to a few thousand documents, especially short documents, there are a few out of the box methods that can yield reasonable results, whether it be embedding clustering or leveraging LLMs for tagging.
However as your (1) datasets gets larger (2) documents expand from tweets and text messages to 30+ minute conversations and (3) you build downstream analytics on top of the learned semantic units, you really start to feel the limitations of LLMs and embedding for reliable annotation. That doesn't even get into the nuances associated with taxonomy management, seasonality, and model drift.
TLDR; this problem solved effectively has a lot of value and is a lot harder than it seems.
- modgate
Extracting feedback from agent conversations is harder than it looks because users don't give explicit feedback — they rephrase their query, which is implicit negative feedback, or they accept the output and move on, which is implicit positive. The signal-to-noise ratio in natural conversation is ~5-10% actionable feedback at best. I'd be interested in how Agnost handles the classification pipeline. Are you using an LLM-as-judge approach (costly but flexible) or a fine-tuned classifier (cheaper but needs retraining)? And how do you handle the cold-start problem where you have no labeled data to train the initial classifier? For enterprise deployments, the integration with existing support workflows (Zendesk, Intercom tickets) is often the deciding factor — can Agnost map agent conversations to existing ticket IDs?
- m_kos
> Rageprompting
Lovely name! I implemented profanity monitoring in my Hermes setup to identify "learning opportunities" for my agents. It is quite useful. If you are budget-conscious, one challenge is determining what is the smallest number of previous rounds that Hermes needs to correctly infer what it did wrong. Curiously, Claude Code is horrible at figuring out what it did wrong. I often read its memories, and they are rarely useful.
- gabriel666smith
I built an in-house version of this a couple of years ago for where I was working. My concern would be that by excluding observability, you might end up creating a really selective dataset, whose conclusions you're then asking companies to take seriously when allocating resources to different possible roadmaps.
My guess would be that agent logs would highlight obvious feature requests and bugs for smaller companies - like customers expecting an AI video editor product to be able to add subtitles to a video by itself.
For larger companies who deal with a higher volume of inbound customer support / agent requests, there will probably be big, noisy, already-known-by-the-team query clusters that make up big portions of the dataset - for example, "billing issue with my subscription". After those big clusters you'll likely have a really long tail of different queries, and - without deep observability - no real way to rank their importance. I also think you'd be unlikely to understand the root cause of the product issue in a complex developed product with lots of users solely from agent logs. Most product teams can't make good product decisions consistently, and they're working with a lot more data.
If coupled with staying out of evals (which, btw, I wouldn't find trust-building, if I were a potential customer of yours), I think that it might be difficult to provide genuine value in this space for larger orgs - without evals it's easily dismissed as just fancy & mostly-contextless s […]
- benswerd
Without using agnost, what are some basic SQL queries I can run on my data to find outliers I'd otherwise be missing?
How far can I get with just keywords, common phrases, boring traditional analysis?
Depending on what I measure there, when is the right time for me to consider upgrading to something like Agnost/what is a specific example of what it will find that traditional/rigid analytics approaches will miss?