AI-Powered Observability and Incident Prediction in Distributed Enterprise Platforms

Abstract

Increasingly complex distributed enterprise platforms have revealed severe limitations of traditional monitoring tools, which cannot correlate heterogeneous telemetry signals or translate low-level anomalies into actionable incident-level insights. While recent progress in log-, metric-, and trace-based machine learning has improved anomaly detection accuracy, research demonstrates there are many remaining challenges in terms of cross-modal correlation, generalization across evolving systems, explainability, and end-to-end incident prediction. Existing deep learning models are oftentimes well-behaved on a single isolated dataset but struggle with concept drift, multi-tenant noise, and dynamic behaviors in microservice architectures. Similarly, most AIOps frameworks provide architectural recommendations with limited rigorous evaluation in operational impact, especially about the reductions in MTTD and MTTR. Root-cause analysis techniques have been advanced through graph and causal modeling. They remain decoupled from proactive incident forecasting and often fail to integrate human-in-the-loop operational knowledge.

This research addresses these shortcomings by developing an integrated AI-powered observability framework that harmonizes logs, metrics, and traces through multimodal representation learning, reinforces temporal and causal reasoning for early incident prediction, and integrates explainable analytics targeted at enterprise-scale decision making. The proposed approach will aim to provide predictive, interpretable, operationally measurable incident management by mapping low-level anomalies to service-level incident likelihood, impact, and probable root causes. This work contributes an empirically validated pipeline aimed at enhancing reliability engineering outcomes and firming proactive resilience strategies in distributed enterprise platforms.