Large Language Model–Driven Intelligent Observability Frameworks for Serverless Applications in Event-Driven Cloud Architectures

Abstract

Observability has also become a core condition of running the developing cloud-native systems, especially within serverless and event-driven systems marked by extreme dynamism, brief execution cycles, and distributed control streams. Conventional observability systems with conventional dashboards and threshold-based alerts, as well as manual root cause investigation, cannot deliver actionable intelligence in these types of environments because of the low level of surrounding awareness and human-in-the-loop feedback. Since serverless platforms can scale to the heuristics of thousands of parallel functions invoked by the heterogeneous event streams, cognitive load on operators grows exponentially, resulting in blind spots in performance monitoring, reliability insurance, and cost management. Large Language Models (LLMs) are a breakthrough in the reasoning of intelligent systems, making it possible through semantic understanding, contextual inferencing and natural language interface with large, heterogeneous telemetry volumes. In the current paper, Intelligent Observability Framework (LLM-IOF) is proposed that would be customized to service-less applications deployed on an event-driven cloud infrastructure using the LLM. Incorporated into the framework, there are distributed tracing, log semantics, metric correlation, and event lineage alongside LLM-based reasoning agents that provide automated anomaly detection, causal inference, incident summarization, and proactive remediation recommendations. The suggested structure proposes a multi-layers observability intelligence pipeline that contains telemetry ingestion, semantic normalization, vectorized context modeling, LLM-based reasoning as well as autonomous feedback loops. In contrast to traditional APM technologies, the framework allows generating hypotheses in real-time, cross-service causal learning, and learning based on historical events. The methodology has supported both reactive and proactive observability whereby the system operations were based on manual debugging rather than cognitive assistance. Representative load experimental analysis of serverless workloads showing a significant improvement in the mean-time-to-detect (MTTD), mean-time-to-resolve (MTTR), and operational efficiency. The findings show that self-healing cloud systems can be supported on the basis of LLM-induced observability frameworks. An optimistic conclusion on the paper would be covering the limitations, security considerations and future research directions with respect to autonomous cloud operations