How to Cite:
Mohan D J, "AI-Optimized Platform Architecture for Cloud-Native Enterprise Systems" International Journal of Emerging Technologies, Computing and Intelligent Systems, Vol. 1, No. 1, pp. 12-16, 2026.

Abstract:
The rapid evolution of cloud computing has transformed enterprise application development through the adoption of cloud-native technologies such as microservices, container orchestration, serverless computing, Infrastructure as Code (IaC), and continuous delivery practices. However, the increasing complexity of distributed cloud environments creates significant challenges related to resource optimization, application reliability, security management, scalability, and operational intelligence. Traditional platform management approaches often depend on predefined rules and manual intervention, limiting their ability to respond dynamically to changing workloads and infrastructure conditions. This research proposes an AI-Optimized Platform Architecture (AIOPA) for cloud-native enterprise systems that integrates Artificial Intelligence (AI), Machine Learning (ML), Kubernetes orchestration, observability frameworks, service mesh technologies, and automated DevOps workflows into a unified intelligent platform. The proposed architecture enables predictive resource allocation, autonomous workload optimization, intelligent fault detection, automated remediation, and continuous performance improvement. The framework analyzes real-time operational telemetry including metrics, logs, traces, and events to generate intelligent decisions for improving application availability and infrastructure efficiency. A comparative evaluation indicates that AI-enhanced platform architectures provide improved scalability, reduced operational overhead, faster incident resolution, and better resource utilization compared with conventional cloud management approaches. The proposed model provides a foundation for next-generation enterprise platforms capable of autonomous optimization, adaptive operations, and resilient cloud-native service delivery.

Keywords: Artificial Intelligence, Cloud-Native Systems, Platform Engineering, Kubernetes, AIOps, Machine Learning, DevOps Automation, Microservices, Cloud Infrastructure Optimization, Autonomous Computing.

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