InsightFinder raises $15M to diagnose failures in systems built on AI agents

InsightFinder has raised $15M to tackle one of the biggest and least-solved problems in enterprise AI: the inability to quickly and accurately diagnose where a system broke when AI agents have taken over real workflows.

When an AI agent makes a mistake or stalls, finding the cause is considerably harder than with a failure in traditional software. The fault may lie in the language model itself, in the data it received, in a tool it called, in an orchestrator managing several agents, in a third-party API, or in the underlying infrastructure everything runs on. InsightFinder has set out to solve exactly this multi-layered diagnostic problem.

According to the company's CEO, Helen Gu, the industry's main challenge today is not simply monitoring individual AI models, but diagnosing how the entire technology stack functions now that AI has become an integral part of it. This is a fundamentally different level of complexity: traditional monitoring systems can track logs, metrics and traces, but cannot interpret the non-deterministic behavior of an LLM within a complex agent chain.

As agent systems spread, a single model is no longer the point of failure. It is now a whole chain: LLM calls, calls to external tools, API integrations, knowledge bases in the form of vector stores, orchestrators managing several parallel agents, and external services. Diagnosing such a system with classic methods means spending hours manually analyzing scattered logs instead of fixing the problem quickly.

InsightFinder specializes in AI observability, a field that is rapidly gaining importance as companies move from experimenting with chatbots to deploying autonomous agents in production systems. The company's platform can not only track calls to AI models but also correlate them with the state of the entire infrastructure, from server load to the response time of downstream services. This gives a full picture of what is happening rather than isolated fragments.

The $15M round will let InsightFinder expand its platform and grow its engineering team. The AI observability market is only taking shape, but competition is already noticeable: Arize AI, LangSmith from LangChain, Weights & Biases, Honeycomb and a number of other players operate in this space. InsightFinder is betting on broader coverage, not only tracing LLM calls but also correlating them with the state of the whole infrastructure, which sets the product apart from narrowly specialized LLM trackers.

For enterprise customers the problem is especially acute. When an AI agent stops working on a Friday evening, the engineering team has to work out within minutes whether the problem lies with the model itself, an expired authorization token, a failed third-party API, or a degraded database. Without specialized tools, this analysis turns into a detective investigation through raw logs, which is expensive and slow.

The investment in InsightFinder reflects a broader industry trend. As AI agents take over real business processes, from customer support to financial operations and supply chain management, the reliability and diagnosability requirements for them are approaching the standards of critical infrastructure. Companies can no longer afford agents that simply make mistakes from time to time for unclear reasons, especially when automated decision-making is involved.

InsightFinder positions itself as an infrastructure layer for the era of agentic AI: a tool without which production deployment of agents in mission-critical processes remains a risky undertaking with unpredictable points of failure.