The Autonomous Reliability Engine: A Unified AI Framework for Self-Healing Enterprise Applications

Tamerlan Mammadzada

Abstract: Enterprise systems today operate in environments where downtime, performance degradation, and operational failures carry severe financial and organizational consequences. Traditional reactive maintenance approaches are increasingly insufficient for meeting modern reliability demands. This article proposes a unified multi-layer AI-driven self-healing architecture that integrates predictive analytics, anomaly detection, causal inference, autonomous remediation, and continuous learning into a cohesive operational framework. The work presents original contributions in architectural unification, lifecycle coordination, comparative evaluation across integration patterns, and a structured implementation blueprint for mission-critical environments. Through analysis of machine learning methodologies, real-world scenarios, and operational best practices, this paper establishes a foundational model for next-generation self-healing enterprise systems. It aims to support researchers, QA engineers, and enterprise technology leaders seeking to operationalize AI-driven resilience at scale.

Keywords: Autonomous Remediation, AI-Driven Maintenance, Predictive Failure Detection, Operational Resilience, Self-Healing Systems

How to Cite?: Tamerlan Mammadzada, "The Autonomous Reliability Engine: A Unified AI Framework for Self-Healing Enterprise Applications", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 1869-1876, https://www.ijsr.net/getabstract.php?paperid=SR251125095445, DOI: https://dx.doi.org/10.21275/SR251125095445