Intent-Based Kubernetes Configuration via LLMs: Current Trends and Open Challenges

The advent of Large Language Models (LLMs) is progressively transforming how complex tasks across various domains can be automated, with a notable potential impact on cloud computing operations. In this domain, LLMs might be used, for example, to configure Kubernetes (K8s) clusters via the generation of manifest files – structured configuration files defining the containerized environment. However, despite the considerable advances in LLMs’ text generation, this task conceals several challenges that prevent operators from achieving a fully automated process. In this paper, we present the current trends in solving these gaps, quantitatively evaluate the accuracy of LLM-based approaches to generate K8s manifests starting from human intents, and discuss open challenges that make benchmarking and automation still complex. Experiments over three open-source LLMs demonstrate how intent-based K8s manifest generation can be effectively achieved through model fine-tuning, but also what open issues remain and must be addressed prior to having an autonomous and self-healing K8s infrastructure managed via Agentic AI.