The role of observability tools has evolved once again. While the market for solutions to ensure tech systems’ reliability has grown over the years, the center of gravity has steadily shifted from ‘track everything’ to ‘control complexity and costs.’ Meanwhile, the rapid influx and adoption of AI agents within enterprises have only added a brand new category of workload that needs to be observed.

InsightFinder AI, a startup based on 15 years of academic research, is no stranger to this problem. The company has been using machine learning to monitor, identify, and proactively fix IT infrastructure issues since 2016, and is now attacking today’s AI model reliability issue with an AI agent solution that can do everything from detection and diagnosis to remediation and prevention.

According to Gu, the biggest problem facing the industry today is not just monitoring and diagnosing where AI models go wrong; it’s diagnosing how the entire tech stack operates now that AI is a part of it. 'In order to diagnose these AI model problems, you need to actually monitor and analyze the data, the model, and the infrastructure together,' Gu told TechCrunch. 'It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s simply your infrastructure.'

InsightFinder’s newest product, dubbed Autonomous Reliability Insights, can do all this by using a combination of unsupervised machine learning, proprietary large and small model language models, predictive AI, and causal inference.

The observability space is crowded with contenders for a share of the new market that’s been opened up by the influx of AI tools. But Gu isn’t fazed. On the contrary, she claims the InsightFinder’s expertise, experience, and customizability act as a sufficient moat.