# Agnost AI: production analytics for agent failures

Canonical: https://thelazyweb.com/companies/agnost-ai/
Breadcrumb: [Home](https://thelazyweb.com/) / [Companies](https://thelazyweb.com/companies/) / [Agnost AI: production analytics for agent failures](https://thelazyweb.com/companies/agnost-ai/)
Author: Ali Abouelatta (https://thelazyweb.com/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

## What it does

Agnost AI watches production conversations and traces from AI agents to find silent failures, user frustration, policy violations and recurring unmet intents. It clusters the evidence, links each finding back to the underlying conversation and tool calls, and helps teams turn the failure pattern into an eval or a model improvement. The [current homepage](https://agnost.ai/) and [docs](https://docs.agnost.ai/) make the distinction from ordinary observability clear: a trace can say “success” while the user still received nothing useful.

The fit is an AI product team with enough real traffic to see repeated failures but not enough time to review every transcript manually. Agnost is strongest where teams want a quality loop from user intent to concrete fix, not just latency and token dashboards.

## Why I’d look closer

The homepage says teams should send the conversations they choose, use pseudonymous IDs and redact secrets or sensitive fields before ingestion; it explicitly says Agnost

[Agnost AI: production analytics for agent failures](<https://www.thelazyweb.com/companies/agnost-ai/>)

Source observed: 2026-09-20T02:21:46.714Z. Last checked: 2026-10-08T02:09:22.946+00:00.

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