KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management

作者：Mohsen Seyedkazemi Ardebili, Andrea Bartolini · 发表于：Journal of Grid Computing · 年份：2025 · DOI：10.1007/s10723-026-09837-6 · 被引用次数：10 · 研究领域：Computer Science

Kubernetes has become the foundation of modern cloud-native infrastructure, yet its operational complexity remains a persistent barrier. Administrators must navigate a vast API surface, manage heterogeneous workloads, and coordinate tasks across disconnected tools—often requiring precise commands, declarative configuration files, and deep domain expertise. This paper presents KubeIntellect, a Large Language Model (LLM)-powered system for end-to-end Kubernetes management through natural language. KubeIntellect spans all major categories of Kubernetes operations—read, write, delete, exec, access control, and lifecycle management—through a supervisor-coordinated set of domain-specialized agents, with human-in-the-loop (HITL) confirmation on all mutating operations. Operations outside the static tool set are handled by the Code Generator Agent, which synthesizes, validates, and registers new Kubernetes tools at runtime. The Code Generator Agent executes synthesized tools in an in-process Python REPL rather than a separate process or container; process-level isolation between synthesized code and the host runtime is therefore not enforced, and a defense-in-depth model comprising static analysis, API-call validation, and mandatory human-in-the-loop review constitutes the primary mitigation. Migration to pod-level isolation is a planned hardening step. Evaluation on a live four-node Kubernetes cluster (170 pods across 18 namespaces) shows: a 75% pass rate (12/16; 95% CI: 51%–91%; me...