Towards Agentic OS: An LLM Agent Framework for Linux Schedulers

YUSHENG ZHENG, YanPeng Hu, Wei Zhang, Andi Quinn

Keywords: Agent, LLM, Linux kernel, eBPF

Abstract:

Operating system schedulers suffer from a fundamental semantic gap, where kernel
policies fail to understand application-specific needs, leading to suboptimal perfor-
mance. We introduce SchedCP, the first framework that enables fully autonomous
Large Language Model (LLM) agents to safely and efficiently optimize Linux
schedulers without human involvement. Our core insight is that the challenge is
not merely to apply a better LLM, but to architect a decoupled control plane that
separates the AI’s role of semantic reasoning ("what to optimize") from the sys-
tem’s role of execution ("how to observe and act"). Implemented as Model Context
Protocol(MCP) server, SchedCP provides a stable interface with three key services:
a Workload Analysis Engine, an evolving Scheduler Policy Repository, and an
Execution Verifier that validates all AI-generated code and configure before deploy-
ment with static and dynamic analysis. We demonstrate this architecture’s power
with sched-agent, a multi-agent system that autonomously analyzes workloads,
synthesizes custom eBPF scheduling policies, and deploys them via the sched_ext
infrastructure. Our evaluation shows that SchedCP achieves up to 1.79x perfor-
mance improvement and 13x cost reduction compared to naive agentic approaches,
all while maintaining high success rate. The code will be open-sourced.

Submission Number: 32