Error detection and debugging for autonomous agents

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

Problem

AI agents fail implicitly—completing all calls successfully while making errors like clicking wrong buttons or citing wrong documents. Teams struggle to detect these errors at scale, and existing observability solutions either just collect traces without analysis or use expensive large models to inspect them.

Solution concept

A specialized error detection system trained to analyze agent execution traces end-to-end, identifying implicit failures without requiring expensive large models. The product would automatically cluster similar errors, enable users to build evaluation datasets from error cases, and integrate with agent development workflows to verify fixes before production release.

Who has it

Teams building and running autonomous AI agents in production, including those in software development, legal, and customer support.

How they handle it today

Today, teams either manually inspect agent traces (time-consuming, doesn't scale) or use existing observability platforms that collect traces but provide limited automated error detection, or rely on expensive large language models to analyze traces, making it cost-prohibitive to analyze every production trace.

Why now

Autonomous AI agents are moving into production at scale across multiple industries, creating urgent need for production monitoring and debugging tools. Existing observability solutions built for traditional microservices don't handle agent-specific failure modes.

How it could charge

Subscription service charging per analyzed trace or per month based on trace volume processed, with a self-hosted open-source version and managed cloud offering.