Operating system schedulers suffer from a fundamental semantic gap, where kernel policies fail to understand application-specific needs, leading to suboptimal performance. 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 system's role of execution ("how to observe and act"), thereby separating the optimization problem into two stages: goal-inference and policy-synthesis. 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 deployment 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 an 1.79x performance improvement, and a 13x cost reduction compared to naive agentic approaches, all while maintaining high success rate. By bridging the semantic gap, SchedCP democratizes expert-level system optimization and represents a step towards creating truly self-optimizing, application-aware operating systems. The code is open-sourced in https://github.com/eunomia-bpf/schedcp

翻译：操作系统调度器存在根本性的语义鸿沟，即内核策略无法理解应用特定需求，导致性能欠佳。我们提出了SchedCP，这是首个支持大型语言模型（LLM）智能体在无需人工干预的情况下安全高效优化Linux调度器的全自主框架。我们的核心洞见在于：挑战不仅在于应用更优的LLM，更在于构建解耦的控制平面，将AI的语义推理职责（“优化什么”）与系统的执行职责（“如何观测与执行”）分离，从而将优化问题分解为目标推断与策略合成两个阶段。SchedCP通过实现为模型上下文协议（MCP）服务器，提供包含三个关键服务的稳定接口：工作负载分析引擎、持续演进的调度器策略库，以及通过静态与动态分析在部署前验证所有AI生成代码与配置的执行验证器。我们通过sched-agent展示了该架构的强大能力——这是一个能自主分析工作负载、合成定制化eBPF调度策略并通过sched_ext基础设施进行部署的多智能体系统。评估表明，相较于原始智能体方法，SchedCP最高可实现1.79倍的性能提升与13倍的成本降低，同时保持高成功率。通过弥合语义鸿沟，SchedCP实现了专家级系统优化的普及化，标志着向构建真正自优化、应用感知型操作系统迈出了关键一步。代码已开源：https://github.com/eunomia-bpf/schedcp