How Far Can Root Cause Analysis Go on Real-World Telemetry Data?

Abstract

Root cause analysis (RCA) of microservice failures requires reasoning over telemetry far larger than a model's context, so a language agent reasons over extracted evidence. We start from an error analysis of such an agent on OpenRCA. A reverse reasoning agent reconstructs an evidence chain to each labelled cause and compares it against the agent's own trace, locating failures where a grounded chain existed but was not followed; its labels agree with two site reliability engineers. Being label-conditioned, it audits evidence rather than testing forward identifiability, leaving open what the agent lacked: procedure or telemetry-specific knowledge. Existing systems cannot separate them, extracting evidence inside the reasoning loop. We compute the evidence once, offline, and supply it identically to every arm. Rules mined from the audit reports of one Market cloudbed raise Full Score on a held-out cloudbed from 14.10 to 35.90, surviving correction for multiple testing, against 24.36 for manual rules and 19.23 for a label-and-schema control. The rules add 17 solved cases on top of the five-step procedure but 11 without it, the repairs overlapping. Removing the procedure raises Full Score in all three conditions tested, though no contrast survives correction. Deleting only its earliest-onset criterion yields a net gain of 8 cases against 11 for the whole procedure, which we report descriptively. Mining is label-supervised, and all results are specific to OpenRCA.