Predictive and Causal Intelligence in Cloud-Native Enterprise Platforms through AI and ML Driven Interoperability Root-Cause Analysis and Performance Optimization
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Abstract
The rapid adoption of cloud-native architectures has transformed enterprise platforms by enabling scalability, agility, and continuous delivery. However, the increasing complexity of distributed microservices, heterogeneous data sources, and dynamic workloads introduces significant challenges in observability, interoperability, and performance management. Traditional monitoring and analytics techniques are insufficient to proactively identify system anomalies or explain the root causes of failures in real time. This paper proposes a predictive and causal intelligence framework for cloud-native enterprise platforms that integrates artificial intelligence (AI) and machine learning (ML)–driven interoperability, root-cause analysis, and performance optimization. The framework combines predictive analytics, causal inference, and intelligent observability to enable proactive system management and automated decision-making. Through architectural analysis and domain-driven design principles, the paper demonstrates how enterprises can achieve resilient, explainable, and optimized cloud-native operations. The proposed approach supports next-generation enterprise platforms across domains such as finance, healthcare, and large-scale digital services.
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