Agnost AI
Product analytics for conversational AI agents — catches silent failures like hallucinations and broken promises.
| What is it | Product analytics for conversational AI agents — catches silent failures like hallucinations and broken promises. |
|---|---|
| Pricing | Free |
| Free tier | Yes |
| Platform | Web Application |
| Best for | Identifying and resolving issues in conversational AI agents, Improving user experience for AI-powered chatbots and assistants |
| Domain registered | 2025 |
Data updated Sept. 19, 2026
What does Agnost AI do?
Agnost AI is a product analytics platform built specifically for conversational AI agents. It monitors your agent's conversations with users to detect failures that traditional observability tools miss. While a trace might show a successful API call, the user may still receive a hallucinated answer or a broken promise. Agnost catches these silent failures by analyzing every conversation alongside the corresponding trace, identifying where users get frustrated, where the agent fails, and what improvements are needed.
Getting started is simple: you connect Agnost to your agent using a one-line command — `npx skills add AgnostAI/skills --skill agnost-ai`. After that, it automatically ingests conversations and uses AI to cluster them into recurring problems, detect intents and sentiment, and flag violations related to quality, policy, or compliance. You can explore a live demo without signing up. Each insight links directly to the conversation and trace, so you can investigate and then follow built-in self-improvement suggestions to fix issues.
This tool is ideal for teams building and maintaining conversational AI agents — whether a customer support chatbot, a sales assistant, or an internal AI tool. Product managers and engineers can use it to prioritize the most impactful fixes, reducing churn and improving user satisfaction. Real-world use cases include detecting hallucinations, tracking broken promises (like an agent that says 'I'll send you that PDF' but never delivers), and spotting policy violations before they escalate.
Key features
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