An Expert in Residence: LLM Agents for Always-On Operating System Tuning

Classical machine-learning auto-tuners for OS control struggle with semantic gaps, brittle rewards, and unsafe exploration. We introduce an online, LLM-driven agent that emulates expert reasoning for continuous OS optimization. When tuning the Linux Completely Fair Scheduler’s hyperparameters, the agent outperforms Bayesian optimization by 5% in single-parameter tuning, 7.1% in two-parameter co-tuning, and a human expert by 2.98% overall, while converging faster and adapting more quickly to workload changes. When application counters are unavailable, system-level proxies (e.g., Instructions Per Cycle (IPC)) preserved tail latency in our setup. Putting this together, we propose adopting the Model Context Protocol (MCP) for tool/resource discovery and invocation and a logging channel; on top of that, we propose adding transactional apply--commit--revert, host-mediated approval gates, and policy controls in the OS-tuning server and host to ensure safe, auditable operation. Our results and reference design suggest a practical path toward safe, self‑adapting OS control.