Agnost AI

Agnost AI is product analytics for conversational agents. It ingests production conversations and traces, then looks for failures that ordinary infrastructure metrics can miss: users repeating themselves, silent task failures, hallucinated links, behavior drift, frustration, feature requests, churn signals and policy or quality violations.¹

The current product groups recurring patterns, links each insight back to the exact users and conversations behind it, and can turn discovered problems into reviewed suggestions, evals and agent improvements. Integration is positioned as lightweight through an Agnost skill, SDK-style instrumentation and OpenTelemetry.¹²

A newer Y Combinator description extends the thesis from analytics into workload-specific model optimization: Agnost says it can use historical traces to build held-out eval sets, train specialist models and compare them against the existing frontier model on task success, latency and cost. The published customer numbers are first-party case evidence rather than an independent benchmark.³

Agnost has a current Free plan covering up to 1,000 events per month with 7-day retention. Starter is $49 per month for up to 10,000 events and 30-day retention. Pro is $499 per month for up to 1 million events and 90-day retention. Enterprise is custom and adds custom volume and retention, self-hosted VPC deployment, audit logs, custom SLAs/SLOs and custom improvement workflows.¹

This structure makes the product inexpensive to test on an early-production agent while keeping high-volume pricing predictable at the Pro tier. The meaningful unit is event volume and retention rather than seats, so teams should estimate how many user/agent events their real conversations generate before comparing tiers.

Agnost is a Summer 2026 Y Combinator company and Product Hunt confirms the same current positioning around production conversation analytics. The Product Hunt launch describes analysis of more than one million messages per day, but the page is still a launch/community surface rather than a mature review corpus.²³

The Agnost Trust Center currently shows SOC 2 Type 1 as compliant and SOC 2 Type 2 as in progress, together with a published set of security policies and monitored controls. That is a useful current governance signal, although procurement teams should review the underlying documents and deployment model for their own requirements.⁴

The wider independent evidence base remains sparse. Current source-specific searches across major software-review platforms, technology publications and benchmark sources did not surface an exact-product rating or controlled test. That absence limits confidence but is not evidence of poor performance.

Agnost's own YC launch post reports a specialist-model customer result with 22.9% higher task success, 90.2% lower median latency and 94.5% lower cost than the cited frontier-model baseline. Those figures are decision-relevant but remain company-reported and workload-specific, so they should not be generalized to unrelated agents.³

Agnost is best suited to teams with a meaningful stream of real user conversations where manually reading traces no longer scales. Product, engineering and AI teams can use it to identify recurring failure patterns and unmet needs that are visible in conversations but not necessarily in HTTP success codes or static eval suites.

It is less useful for prototypes without production traffic, non-conversational agents with little user interaction, or organizations that need independently benchmarked reliability before adoption. Teams interested in the specialist-model layer should validate it as a separate workload-specific optimization exercise rather than assume the analytics product automatically reduces model cost.