---
title: "Agnost AI: Automatically Extract User Feedback from Agent Conversations, Catch Failures Evals Miss"
source: "Hacker News 热门（Algolia · 中文化）"
source_tier: "t2"
url: "https://agnost.ai"
permalink: "https://feed.astralor.com/items/itm_fdcd2536dbd4ce9c320cfc5c"
published: "2026-07-14T16:06:18.000Z"
score: 74
channel: "ai"
tags: ["Agnost AI","AI Agent Monitoring","YC S26","User Feedback Extraction"]
---

# Agnost AI: Automatically Extract User Feedback from Agent Conversations, Catch Failures Evals Miss

## AI commentary

> As an editor, I believe Agnost AI hits the real pain point of AI agent deployment: implicit feedback in production. For any team deploying agents, this tool can significantly reduce manual debugging costs. It will be interesting to see how it balances auto-extraction with human review, and whether it will deeply integrate with mainstream LLM platforms in the future.

## At a glance

Agnost AI continuously analyzes production AI agent conversations, automatically extracts user feedback, and catches agent failures that standard evals miss. It transforms high-impact patterns into reviewed fixes. Trusted by Google and 25+ AI teams, it helps teams quickly identify and fix critical issues by deeply understanding user-AI interactions, improving agent reliability and user experience.

## Key points

- Continuously analyzes production AI agent conversations and auto-extracts user feedback
- Catches agent failure patterns that standard evals miss
- Transforms high-impact patterns into reviewed fixes
- Trusted by Google and 25+ AI teams

## Full text

Agnost AI continuously analyzes production conversations, catches agent failures your evals miss, and turns the highest-impact patterns into reviewed fixes. Trusted by Google and 25+ AI teams.

---

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