International Journal of Science, Engineering and TechnologyAuthors: Ramani Teegala
Abstract: By September 2022, cloud-native systems operating at enterprise and internet scale had reached a level of architectural and operational complexity that fundamentally challenged traditional approaches to incident detection, diagnosis, and response. Microservices proliferation, dynamic infrastructure provisioning, continuous deployment pipelines, and deeply interconnected service dependencies produced failure modes that were increasingly emergent rather than deterministic. While observability platforms provided extensive access to logs, metrics, and distributed traces, the practical bottleneck during incidents shifted from data availability to human sense making. Incident response workflows continued to rely heavily on manual correlation, institutional memory, and ad hoc reasoning performed under severe time pressure, resulting in prolonged mean time to diagnosis and inconsistent operational outcomes. During this period, advances in large language models demonstrated a growing capacity to interpret, summarize, and synthesize natural language and semi-structured information. These capabilities aligned closely with the nature of operational artifacts such as alerts, logs, incident timelines, architectural documentation, and post-incident analyses. This paper introduces the concept of LLM-powered incident intelligence as an emerging operational discipline appropriate to the state of industry practice as of September 2022. LLM-powered incident intelligence refers to systems that apply large language models, constrained by retrieval, governance, and human-in-the-loop design principles, to assist operators in understanding and reasoning about complex incidents rather than executing remediation autonomously. The paper positions LLM-powered incident intelligence as a cognitive augmentation layer that sits between observability tooling and human decision making. Rather than replacing human judgment, these systems aim to reduce cognitive load, accelerate contextual understanding, and support evidence-driven reasoning during high-severity incidents. The discussion is grounded in the maturity of transformer-based language models, semantic retrieval techniques, and enterprise observability platforms available by late 2022. Security, operational correctness, and accountability are treated as first-class constraints. By framing LLM-powered incident intelligence as an assistive and governed capability, this paper outlines a pragmatic approach to enhancing incident response effectiveness without undermining trust or operational control.
DOI: http://doi.org/10.5281/zenodo.18694701
