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The best alternatives to Agnost AI include Helicone. Each offers unique features for different use cases.

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Agnost AI

Analyzes production agent conversations to find the failures your evals miss, then proposes reviewed fixes

freemiumDR 37The site offers a self-serve 'try now' entry point and docs but publishes no tier pricing as of July 2026, so cost above the trial is a sales conversation.View full pricing →

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About Agnost AI

Agnost AI is product analytics for teams building conversational agents, built around a specific gap: your evals pass and production still fails. It continuously analyzes real production conversations to find where users get stuck, get frustrated, or fail to convert, clusters those into patterns, ranks them by impact, and turns the highest-impact ones into fixes your team reviews rather than a list of anomalies someone has to triage. The distinction from a standard observability tool is that Agnost is reading the conversation as a user experience, not the trace as a system event — the failures it surfaces are the ones where nothing errored and the agent still lost the user. A second output falls out of the same analysis: unmet demand. One customer describes discovering 1,247 feature requests sitting inside chats they had already had, for features they didn't have and didn't know people wanted. The company also reports autonomous pull requests, with 16 of 18 merged at one customer, so the loop runs from production signal through to proposed code change. Public references are unusually concrete for an early company — a Google engineer describing observability integrated into MCP Toolbox for Databases, a member of technical staff at Exa on analytics and error-rate tracking, and a GTM lead at Corgi Insure reporting that voice BDRs booked more meetings once Agnost surfaced which conversation patterns actually converted.

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Key Features

✓Continuous analysis of production conversations for stuck, frustrated, and non-converting users

✓Failure patterns ranked by impact instead of raw anomaly lists

✓Reviewed fixes and autonomous pull requests generated from production signal

✓Feature-request mining from conversations users already had

✓Conversion-pattern analysis for sales and support agents

✓Error-rate and analytics tracking across agent deployments

Agnost AI Pros & Cons

✅ Pros

+Targets silent failures — the ones where nothing errored and the user still left
+Named references at Google, Exa, and Corgi Insure rather than anonymous logos
+Feature-request mining is a genuine second use for the same data