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

agnost.ai

July 14, 2026

2 min read

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48/100

Summary

Agnost AI identifies missed failures in real conversations and provides actionable insights for team review. It has integrated observability features into MCP Toolbox for Databases, enhancing analytics and error tracking.

Key Takeaways

Agnost AI identifies missed failures in real conversations and provides actionable insights for improvement.
The platform integrates with any LLM and framework, requiring only a 2-minute setup.
Agnost AI enhances agent performance by analyzing conversation patterns and automatically generating fixes for identified issues.
Users report significant improvements in analytics tracking and error detection, leading to better decision-making and faster development cycles.

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Coverage & discussions

Hacker News · agnost.ai

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

Community discussions

Hacker News discussion ↗

Historical count: 5 · 5 saved in sample · Historical sample

Analysis ready

Community Sentiment

Mixed

Positives

There's a strong belief that conversation logs can reveal hidden product signals that traditional observability overlooks — think insights from frustrated users that can guide feature development.
The challenge of managing large datasets is being tackled with LLMs and embeddings, showing that innovative methods can still yield results even in complex scenarios.
Creative solutions like profanity monitoring in AI setups are being highlighted as valuable learning tools, proving that even budget-conscious developers find ways to innovate.

Concerns

Skeptics argue that relying on these tools might create selective datasets, raising concerns about the validity of insights drawn from such analyses.
There's a sentiment that the extraction of user sentiment from agent conversations is overly complicated, with worries that the AI might misinterpret user feelings.
Commenters express frustration that traditional SQL and keyword searches can miss nuanced user feedback, suggesting that the proposed solutions may not sufficiently address core issues.