AI-Driven Root Cause Analysis In Real-Time Distributed Systems

Abstract

Root cause analysis (RCA) in real-time distributed systems remains one of the most operationally expensive challenges in modern platform engineering. Operational evidence is fragmented across service logs, event streams, dependency graphs, deployment histories, and changing interface contracts. No single observability surface captures the complete causal story of a production incident. This article argues that AI-driven diagnosis becomes reliable only when built on four platform foundations: trustworthy service-state context, task-oriented diagnostic interfaces, machine-readable schema and change awareness, and structured operational memory with semantic retrieval. A five-layer diagnostic architecture is proposed and evaluated against empirically reported results drawn from production deployments and controlled experimental studies in the microservice failure diagnosis literature. The evaluation demonstrates consistent performance advantages for multimodal and graph-based diagnosis approaches over single-modality and threshold-based methods, with production deployment results revealing the practical cost of incomplete or stale diagnostic context. The central conclusion is that AI-driven RCA should not replace human expertise, but can serve as a scalable accelerator for evidence gathering and hypothesis formation in complex distributed environments, provided it is built on strong platform foundations.