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

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

#61

Post by laalshaitaan » Sun, Jul 19, 2026, 11:41 PM UTC

teknokeras wrote: post #60

Congrats on the launch. Question about the data side: conversations tend to contain a lot of PII (names, emails, account details users paste in). When you cluster and surface example messages in the dashboard, is there any redaction step before storage/display, or is that left to the customer to filter before sending through the SDK? Curious how much of SOC 2 scope covers the message content itself vs the infrastruct…

that on the customer's side to redact the PII as you are the ones sending it and you'd know better than us

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

#62

Post by laalshaitaan » Sun, Jul 19, 2026, 11:43 PM UTC

rajeevbakshi wrote: post #59

The distinction between observability and discovery is important. Observability answers "what broke" — discovery answers "what should we build". How you handle sessions that span multiple interactions or context resets? A user might express frustration in message 3 of a conversation but the root cause was a tool failure in message 1 that the agent recovered from poorly!

ai slop again

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

#63

Post by modgate » Tue, Jul 21, 2026, 8:29 AM UTC

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?