Agnost AI
Catch agent failures your evals miss
In plain words
Agnost AI is product analytics software for conversational AI agents. It analyzes production conversations to identify silent failures, hallucinations, user frustration, and behavior drift, then groups these issues into recurring patterns linked to specific conversations and traces. The tool helps teams spot what frustrates users, discover feature requests, and catch policy violations, turning findings into actionable fixes and evaluations.
written from the facts on this page · September 2026
From the sources
Agnost AI analyzes conversations between users and your production AI agents and discovers: silent failures, agent behavior drift, hallucinations, user frustration, hidden feature requests, and churn signals. It groups them into recurring patterns, shows the exact users and conversations behind each insight, and turns them into evals and fixes.
Product analytics for conversational agents. Find silent failures, user frustration, feature requests, and policy violations across every conversation.
See what frustrates users, what they ask for, and what to improve across every conversation. Find user frustration. See where your agent fails, users get frustrated, and churn begins. Every pattern links to the exact conversations and traces so you can fix it fast. Auto-cluster conversations. Turn thousands of chats into the recurring problems that disappoint users, ranked by impact and ready to investigate. Prevent costly failures. Catch hallucinations, broken promises, and policy violations with the exact conversation and trace behind every failure. Fix what matters. Get the highest-impact fixes handed to you with the evidence, recommended change, and evals needed to ship safely. Connect…from agnost.ai
Pricing, as stated on its site
Free tier, paid plans from $49/mo — Agnost AI offers a freemium model with a free tier (up to 1,000 events/month) and paid plans starting at $49/month for Starter tier, $499/month for Pro tier, and custom pricing for Enterprise.
checked September 2026 · prices change
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 9d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com