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Agnost AI catches the production agent failures your evals miss. It reads real conversations, turns them into intent signals, frustration patterns, SOP violations, and failure categories, then helps your team understand what to fix next. Most observability tools tell you a span took 3.2 seconds. Agnost tells you the user was frustrated, asked the same thing three times, dropped off, and which tool call caused it.

Start in five minutes

Send your first MCP tool call, conversation turn, or OTel trace to Agnost.

Review data handling

Understand what Agnost receives and how to redact or pseudonymize sensitive data.

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Signals: what users actually want

Every message is classified into intents and sentiment, then aggregated. The Signals dashboard shows you, in plain English, what users keep asking for, what they love, what they hate, and what’s missing from your product. Agnost AI Signals dashboard You’ll see:
  • Intent clusters: automatic grouping of every conversation by what the user was trying to do.
  • Sentiment over time: how user satisfaction trends day-by-day, broken down by intent or feature.
  • Hidden feature requests: things users keep asking for that you don’t yet ship.
  • Recurring frustrations: repeated questions, abandoned flows, failed tool calls, refusals, and dead ends.

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How it fits in your stack

Your AI Agent  →  Agnost AI  →  Signals · Evals · Improvements
Agnost is OpenTelemetry-native and works with any LLM and any framework. Use the Quickstart, then pick the SDK or framework integration that matches your stack.