Agnost AI

Product analytics for conversational AI agents that surfaces silent failures, frustration, and policy violations.

Overview

Agnost AI is a product analytics platform built specifically for AI agents and conversational chatbots. It ingests conversation traces (via OpenTelemetry or an SDK) and analyzes them to surface silent failures, user frustration, hallucinations, policy violations, and feature requests that traditional observability tools miss because they only track technical success rather than whether the user actually got what they needed.

Beyond analytics, Agnost clusters recurring conversation patterns by impact, links every insight back to the underlying conversation and trace, and suggests fixes with supporting evidence. The company (Y Combinator-backed, Summer 2026 batch) has also begun using the same trace data to train workload-specific models intended to replace frontier models for narrow, repetitive agent tasks at lower cost and latency.

The product is aimed at teams running production conversational agents who want visibility into real user experience beyond simple trace success/failure signals, and who may later want to reduce inference costs by training a specialist model on their own traffic.

Key features

Automatic intent and sentiment detection
Auto-clustering of conversations into recurring problems ranked by impact
Silent failure and rising-friction alerts
Detects hallucinations, broken promises, and policy violations
Links every insight to exact conversation and trace
Self-improvement suggestions for agents
Custom workload-specific model training from production traces (early feature)
OpenTelemetry and SDK-based ingestion

Pros

Purpose-built for conversational AI agents rather than generic analytics
Free tier available to start immediately
Links insights directly to underlying conversations and traces
Backed by Y Combinator with active founder involvement
Simple setup via existing traces/events, no agent rebuild required

Cons

Free tier limited to 1,000 events/mo and 7-day retention
No automatic PII redaction, users must handle sensitive data themselves
Self-hosted VPC deployment only available on custom Enterprise plan
Early-stage product from a small (2-person) team

Use this if

You run a production AI chatbot or agent and want to detect silent failures, user frustration, and policy violations beyond what standard tracing/observability shows.

Skip this if

You don't run a customer-facing conversational AI agent in production, or you only need generic trace/observability tooling rather than user-experience analytics.

Best for

AI product teams shipping conversational agentsTeams debugging silent failures in chatbotsFounders wanting to reduce churn from agent frustrationTeams training custom models from agent traces