Generative Knowledge-Graph Assistants for Service-Desk Incident Triage

Vasudevan Ananthakrishnan (), Srinivas Bangalore Sujayendra Rao () and Sayantan Bhattacharyya ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 5, issue 1, 564-580

Abstract: Modern enterprise service desks are burdened with high incident volumes, inconsistent triage accuracy, and extended resolution times. This paper introduces a novel framework combining generative AI with dynamic knowledge graphs to streamline incident triage in IT service management (ITSM) environments. The proposed system leverages real-time graph-based context retrieval from CMDBs, historical tickets, and dependency maps to enable LLM-driven agents to understand, classify, and route incidents with minimal human intervention. By integrating graph embeddings with generative prompting strategies, the assistant adapts to evolving service environments and improves root-cause correlation. Evaluations on real-world ITSM datasets demonstrate a 73% reduction in triage time, a 41% increase in first-call resolution accuracy, and significant gains in knowledge reuse. The results underline the potential of agentic knowledge-graph architectures to transform reactive service desks into proactive resolution engines.

Keywords: Generative AI; Knowledge Graphs; ITSM; Service Desk Automation; Incident Triage; Large Language Models; Root-Cause Analysis; Graph Embeddings; CMDB (search for similar items in EconPapers)
Date: 2024
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