Launch HN: Agnost AI (YC S26) – Extract user feedback from agent conversations

Hey HN, we’re Shubham & Parth, childhood friends building Agnost AI ( https://agnost.ai ), product analytics for teams building chat and voice agents. We read production conversations and find behavioral failures like users rageprompting (cursing at the agent), repeatedly rephrasing the same request, correcting the agent, asking for missing features, or leaving after an answer that was technically successful. We have an interactive demo with no signup here: https://app.agnost.ai?demo=true Here's a demo video: https://www.tella.tv/video/agnost-ai-launch-hn-demo-9haa The core problem is that chat and voice products do not have the same metrics as web apps. When the product interface is language, clicks and funnels become much less useful. Users also rarely give explicit feedback, and when they do it's usually sugarcoated. I barely type /feedback in Claude or Codex myself. Most users just curse, ask again, correct the agent, or leave. So product engineers get technical visibility from latency, errors, and traces, but still have to guess whether users got what they wanted. We got here after building around agents for the last year and got a couple of founders asking for something like a PostHog for conversations for the AI assistants they were building. We are not trying to be in the observability or evals space. Observability tells you what happened technically. Evals validate cases you already know. We're more on the discovery side like what users wanted, where they got frustrated, what they asked for repeatedly, and what new evals should exist. Teams send us agent conversation messages through SDKs or OTel, optionally with metadata like account, plan, source, organization, etc. We cluster conversations into product-specific intents. Feature requests and bugs are default categories; most other clusters are created dynamically from the customer’s data and evolve over time. You can create your own cluster in plain English. If a cluster gets too broad, we split it. If a