AGENTIC AI-DRIVEN AUTONOMOUS DATA PLATFORMS FOR SCALABLE CLOUD-NATIVE ARCHITECTURES

Abstract

The rapid evolution of artificial intelligence has catalyzed a paradigm shift in how data platforms are designed, deployed, and managed at scale. This paper investigates the integration of agentic AI systems within autonomous data platforms built upon cloud-native architectures, exploring how intelligent agents can independently orchestrate data pipelines, optimize resource allocation, and adapt to dynamic workload demands without continuous human intervention. Agentic AI, characterized by goal-directed reasoning, autonomous decision-making, and multi-agent collaboration, presents transformative potential for eliminating operational bottlenecks inherent in traditional data infrastructure. By leveraging containerization, microservices, and serverless computing paradigms, these platforms achieve elastic scalability while maintaining fault tolerance and cost efficiency. This research examines architectural frameworks that enable AI agents to perform self-healing, predictive auto-scaling, intelligent data governance, and real-time anomaly detection across distributed cloud environments. Furthermore, the paper discusses challenges including agent alignment, latency constraints, security vulnerabilities, and interoperability across heterogeneous cloud ecosystems. Experimental analysis demonstrates that agentic AI-driven platforms significantly outperform conventional rule-based automation in throughput, resource utilization, and mean time to recovery. The findings contribute a robust architectural blueprint for enterprises seeking to modernize data infrastructure through intelligent autonomy.