InsightFinder raised $15M for diagnosing failures in AI-agent systems

InsightFinder attracted $15 million in investments to solve one of the main and least solved problems in corporate AI — the inability to quickly and accurately diagnose exactly where a system with AI agents managing real work processes has failed. When an AI agent makes an error or stops, finding the cause is significantly more complex than troubleshooting traditional software. The error could be in the language model itself, in the data it received, in the tool it called, in the orchestrator managing multiple agents, in a third-party service API, or in the underlying infrastructure running all of this.

InsightFinder has taken on solving this multilayered diagnostic problem. According to InsightFinder CEO Helen Gu, today's industry's main challenge is not simply monitoring individual AI models, but diagnosing how the entire technology stack functions when AI has become an integral part of it. This is fundamentally a different level of complexity: traditional monitoring systems can track logs, metrics, and traces, but cannot interpret the nondeterministic behavior of LLMs in the context of a complex agent chain.

With the proliferation of agent systems, the point of failure is no longer a single model. Now it's an entire chain: LLM calls, external tool invocations, API integrations, knowledge bases in the form of vector stores, orchestrators managing multiple parallel agents, and external services. Diagnosing such a system using classical methods means spending hours on manual analysis of fragmented logs instead of quickly fixing the problem.

InsightFinder specializes in AI observability — a field that is rapidly gaining importance as companies transition from chatbot experiments 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 response times of downstream services. This allows seeing the complete picture of what's happening rather than isolated fragments.

The $15 million round will allow InsightFinder to expand the platform and grow its engineering team. The AI observability market is only forming; however, competition is already noticeable: Arize AI, LangSmith from LangChain, Weights & Biases, Honeycomb, and several other players operate in this space. InsightFinder is betting on broader coverage — not just tracing LLM calls but also correlating with the state of the entire infrastructure, which distinguishes the product from narrowly specialized LLM trackers.

For corporate clients, the problem is particularly acute. When an AI agent stops working on a Friday evening, the engineering team must understand within minutes: is this a problem with the model itself, an expired authorization token, a failed third-party API, or database degradation. Without specialized tools, such analysis becomes detective work on raw logs — expensive and slow.

Investment in InsightFinder reflects a broader industry trend. As AI agents take on real business processes — from customer support to financial operations and supply chain management — requirements for their reliability and diagnosability approach standards for critical infrastructure. Companies can no longer afford agents that simply sometimes fail for unclear reasons — especially when automatic decision-making is involved.

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