AI-Driven OS Level Environment Manager

This preprint presents an AI-driven, agentic operating-system environment manager designed to monitor, configure, and optimize system environments through natural-language interaction and controlled system-level actions. The proposed architecture combines OS-state perception, an LLM-based reasoning agent, a constrained action-execution layer, natural-language interaction, and a safety and audit module. System actions are mediated through a permission policy that can allow or deny proposed operations, while higher-risk operations require explicit user confirmation. Executed actions are logged and rollback mechanisms are provided where inverse operations are available. The proposed evaluation measures task success, safety, unintended-action avoidance, rollback performance, completion time, and user effort compared with manual or scripted environment management approaches.