The hard part of AI root cause analysis is no longer the model (coroot.com)

In a recent analysis of AI root cause analysis (RCA) capabilities, it was revealed that the more challenging aspect is not the models themselves, but the surrounding data and tools—referred to as the "harness." The study highlights that many fails in RCA have less to do with a model's reasoning ability and more with the quality and context of the data provided. Coroot's approach simplifies this by using a deterministic pipeline that correlates signals into findings, allowing the model to focus solely on reasoning without the complexities of tool-calling or incomplete data. The findings from testing several models in a simulated failure scenario show that while larger and more complex models excelled, smaller models could also effectively identify root causes as long as the context was properly prepared. Models like Claude Opus 4.8 and Gemma 4 31B successfully pinpointed issues with a Chaos Mesh experiment disrupting network traffic, showcasing that detailed reasoning is achievable without the burden of excessive telemetry. This indicates a paradigm shift in AI RCA, emphasizing that the preparation of efficient data contexts is crucial, as the reasoning challenge is increasingly becoming less of a hurdle for advanced models.