Find recurring failures and user frustration in agent conversations with linked trace evidence
Agnost AI is a product analytics service for conversational agents. It looks at the relationship between what an agent did and whether the user actually received a useful result. A technically successful trace can still hide a failed experience, such as an unfulfilled promise or a response that missed the user’s question. Agnost is designed to surface these patterns across real conversations instead of requiring a team to inspect each interaction manually.
The platform groups related conversations to identify recurring problems and ranks them by impact. It looks for user frustration, repeated requests and failures that may otherwise be difficult to notice. Each insight links back to the relevant conversations and traces so a developer can investigate the evidence behind the pattern. Quality, policy and compliance violations are also part of the advertised analysis.
Agnost presents recommended improvements and evaluation guidance alongside the detected issue. This supports a workflow in which a product team reviews the evidence, changes the agent or product flow and checks whether the change addresses the observed problem. The service also offers alerts for rising friction and silent failures rather than limiting its output to a one-time report.
The official onboarding path includes an integration skill for adding analytics to an existing agent. Teams can inspect a staging trace before sending broader traffic. Agnost explicitly says it processes the conversation data provided to it and does not automatically remove personal information, so the integration should control which fields are sent.
The free plan supports a limited monthly event volume and short retention. Paid plans increase those limits, while Enterprise adds custom retention and deployment options. The public demo can be explored without signup, giving teams a way to understand the investigation workflow before connecting their own conversations.
