Cloudent: Ai Ai-Agent-Driven Cloud Infrastructure Manager

Abstract

Introduction: The process of cloud infrastructure management remains a high-friction and cognitively intensive endeavor since the Infrastructure-as-Code (IaC) platforms are platform-specific, syntactically dense, and coupled with provider-specific configurations. Since organizations are rapidly moving to cloud-native architecture to ensure they provide scalable and distributed applications, the operational cost of configuring, validating, and maintaining infrastructure has increased manifold. Manual provisioning processes demand extensive technical knowledge and they tend to have repetitive documentation reading, which makes configuration drift, deployment bugs, and ineffective operations more probable. These obstacles pose both a hindrance to enterprise-level efforts at implementing DevOps as well as to students and practitioners in need of a public, low-risk environment in which to learn and test out cloud technologies.

Objectives: This paper seeks to create an AI-agent-based architecture, Cloudent, that provides a translation between natural-language user intent and executable cloud infrastructure by automating the process of IaC generation, enhancing deployment reliability, and cutting operational overhead by generating Intelligent Reasoning and Self-Correcting.

Methods: Cloudent brings together an Agentic AI reasoning engine, based on LangChain and LangGraph, with the Pulumi Automation SDK within a Next.js application to write programs generating type-safe TypeScript IaC without any external CLI. The semantic retrieval layer translates the will of the user into cloud provisioning profiles and an iterative self-healing process translates the deployment logs and feedback on errors and adjusts configurations autonomously. The emulation based on LocalStack provides safe and cost-transparent environment of testing.

Results: Analysis of the suggested framework indicates a high level in automating processes of infrastructure provisioning and still `maintaining contextual accuracy and operational consistency. The combined model recorded training accuracy of 96.50% and validation accuracy of 94.50% in interpreting and executing infrastructure tasks meaning that it was reliable in translating natural-language requirements into deployable resources. The self-healing system minimized the number of debugging cycles, increased the effectiveness of the deployment process, and allowed manual corrections to be performed iteratively, which speeded up the provisioning process and increased trust in automated DevOps operations.

Conclusions: Cloudent illustrates how Agentic AI can change the DevOps processes through transforming the conversational requirements into production-deployable infrastructure. The structure provides a democratized and scalable cloud management solution through autonomous reasoning, correction through iteration and safe emulation, which increases the rate of deployment and improves confidence in operations.