Transforming Cloud Native Enterprise Systems with Agentic Artificial Intelligence and Intelligent Automation Frameworks
DOI:
https://doi.org/10.15662/IJARCST.2025.0805036Keywords:
Agentic AI, Cloud-Native Systems, Intelligent Automation, Microservices, DevOps, Self-Healing Systems, Multi-Cloud, Kubernetes, Autonomous Agents, AIOpsAbstract
Cloud-native enterprise systems have rapidly evolved from monolithic architectures to distributed, containerized, and microservices-driven ecosystems deployed across hybrid and multi-cloud environments. Despite these advancements, enterprises continue to face challenges such as operational complexity, dynamic scaling inefficiencies, observability gaps, and increasing security risks. This paper explores the transformation of cloud-native enterprise systems through the integration of agentic artificial intelligence and intelligent automation frameworks. Agentic AI introduces autonomous, goal-driven software agents capable of perceiving system states, reasoning over contextual data, and executing adaptive actions without continuous human intervention. When combined with intelligent automation frameworks, these agents enable self-healing infrastructures, predictive scaling, autonomous incident response, and continuous optimization of workloads. The study examines how these technologies collectively shift enterprise cloud operations from reactive and rule-based models to proactive, adaptive, and self-governing systems. It further analyzes architectural patterns that support agent orchestration, reinforcement learning-driven optimization loops, and event-driven automation pipelines. By synthesizing current research and emerging industrial practices, this paper highlights the potential of agentic AI to redefine DevOps, SRE (Site Reliability Engineering), and cloud governance paradigms. The findings suggest that enterprises adopting these frameworks can achieve higher system resilience, reduced operational overhead, improved cost efficiency, and accelerated innovation cycles, while also introducing new challenges in trust, explainability, and control mechanisms
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