Agentic Structured Graph Traversal for Root Cause Analysis of Code-related Incidents in Cloud Applications

Authors

Shengkun Cui; Rahul Krishna; Saurabh Jha; Ravishankar K. Iyer

Scores

Rationale

The paper presents a novel approach by integrating LLM-driven graph traversal specifically for the root cause analysis (RCA) of cloud incidents, which is a significant operational challenge. The use of two types of dependency graphs is technically significant as it addresses a key bottleneck in diagnosing complex, code-related issues in cloud environments. Although the methodology is primarily tailored for cloud applications, the underlying concept of structured graph traversal could transfer to other domains involving complex dependency analysis. The idea aligns well with the current momentum in AI for operations and cloud management, though empirical validation is limited to a specific incident set, reducing evidence strength. The approach shows promise for becoming influential in future AI-driven cloud operations solutions.