Find user frustration
Shows where an agent fails, where users get frustrated, and how those patterns relate to the exact conversations and traces behind them.
Agnost AI is product analytics for conversational agents. Detect silent failures, user frustration, feature requests, and policy violations in production conversations.
Agnost AI is a product analytics tool for conversational agents. It is built to help teams see what users are asking for, where they get frustrated, and where an agent fails silently even when traces or system checks look successful.
The product focuses on production conversations and links insights back to the exact conversation and trace so teams can investigate issues, prioritize fixes, and decide what to change in prompts, product flows, or evaluations. The site positions it as useful for catching silent failures, feature requests, and policy or quality violations across real traffic.
Shows where an agent fails, where users get frustrated, and how those patterns relate to the exact conversations and traces behind them.
Groups large volumes of chats into recurring problems so teams can investigate what is happening most often and what is having the biggest impact.
Surfaces hallucinations, broken promises, and policy violations with the supporting conversation and trace for each incident.
Highlights the fixes with the most impact and includes evidence, a recommended change, and evals needed to ship safely.
Connects to existing agent events and conversations in a short setup flow, so teams can start reviewing real traffic without rebuilding their agent.
Useful when an agent appears healthy in traces but users are still abandoning tasks, rage-prompting, or getting no useful outcome.
Helps teams identify repeated complaints, feature requests, or confusing behavior across many chats instead of reading each conversation manually.
Helps teams catch hallucinations, broken promises, and policy violations with evidence attached to the incident.
Supports product and prompt work by showing which fixes are most important and what evidence supports a change.
Gives teams a way to review real traffic after a quick connection process, without rebuilding the agent from scratch.
Agnost AI says it reads conversations alongside traces so teams can catch cases where a trace looks successful but the user still did not get value. The product is positioned for situations where standard observability is not enough to explain user frustration or silent failure.
The site says you can connect the events and conversations you already have, inspect one staging trace to confirm what is being sent, and then turn it on. It also mentions a two-step setup and an example skill install command, but it does not publish a full integration list on the pages provided.
The pricing page lists a free plan and paid Starter, Pro, and Enterprise tiers. The free plan includes automatically discovered intents and sentiment, quality/policy/compliance violations, alerts for silent failures and rising friction, self-improvement suggestions, up to 1,000 events per month, and 7-day retention.
The terms page says Agnost processes the conversation data you choose to send, recommends pseudonymous IDs and redacting secrets or sensitive fields before ingestion, uses HTTPS for transport, and authenticates dashboard access. It also notes that the company does not automatically redact PII for you.
The FAQ and terms indicate that the founders are available to help, and the site invites users to talk to the founders directly. The product is early-stage and positioned as a service where the team may help interpret insights and turn them into prompt, product-flow, or eval changes.
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