Root cause analysis for LLM application failures

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

AI agents fail silently by giving wrong answers instead of crashing, making it extremely time-consuming to diagnose failure patterns. Teams running production AI agents spend hours manually scrolling through traces across hundreds of sessions trying to identify why failures occur.

Solution concept

A tool that automatically collects signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge), processes them to extract facts about each session, clusters similar failure hypotheses across sessions, identifies patterns, and surfaces root causes with suggested fixes for review and application.

Who has it

Engineers and teams running AI agents and LLM applications in production at scale, particularly those with high session volumes.

How they handle it today

Today, when AI agents fail silently, teams manually scroll through traces one by one across hundreds of sessions, trying to find patterns and root causes for wrong answers.

Why now

AI agents and LLM applications are now deployed in production at significant scale (1M+ sessions/day mentioned), creating urgent operational needs around debugging and reliability that did not exist before.

How it could charge

Usage-based pricing tied to number of sessions analyzed or signals processed; tiered subscriptions based on team size and session volume; or premium support for teams running high-volume production agents.