Authors: Alexander Whitmore, Benjamin Clarke, Daniel Harrington, Ethan Montgomery, Naveen Kumar
Abstract: Autonomous Infrastructure Management using LLM-augmented platform engineering frameworks represents a transformative approach to modern cloud operations, combining large language models (LLMs), artificial intelligence, and platform engineering principles to automate infrastructure provisioning, monitoring, optimization, security enforcement, and lifecycle management across hybrid and multi-cloud environments. This research paper explores how LLM-driven automation frameworks enhance Infrastructure as Code (IaC), intelligent orchestration, self-healing systems, predictive analytics, and policy-driven governance to reduce operational complexity and improve infrastructure reliability. The study highlights the integration of natural language processing, machine learning-based anomaly detection, and autonomous decision-making mechanisms that enable adaptive infrastructure management with minimal human intervention. Furthermore, the paper examines the role of AI-powered observability, automated incident response, resource optimization, and compliance validation in accelerating DevOps and AIOps workflows while improving scalability, cost efficiency, and cybersecurity resilience. The proposed framework demonstrates how LLM-augmented platform engineering can streamline enterprise cloud operations through intelligent automation, contextual infrastructure recommendations, and continuous optimization strategies. Finally, the research discusses implementation challenges, ethical considerations, governance requirements, and future advancements in autonomous infrastructure ecosystems, emphasizing the growing significance of generative AI in next-generation cloud-native platform engineering and enterprise infrastructure transformation.
DOI: http://doi.org/10.5281/zenodo.20351661
