Modern OS kernels expose general-purpose but rigid interfaces (e.g., syscalls) that are slow to evolve due to compatibility, performance, and security constraints, leaving apps to pay for mismatched semantics and legacy APIs. Existing kernel mechanism customization approaches (e.g., eBPF) improve flexibility but still face strict safety and performance restrictions and thus require substantial expertise. What's more, they targets customized kernel mechanisms rather than kernel abstractions. This paper presents agentic kernel, a new architecture that decomposes monolithic syscall functions into fine-grained, composable kernel-native micro-syscalls, and then enables Large Language Models (LLMs) to orchestrate them dynamically. Agentic kernel introduces a new entry, isyscall (intent syscall), which lets apps submit high-level intents describing their requirements. Then the kernel leverages LLMs to resolve each intent into composition plan of micro-syscalls to satisfy the user's goal. LLMs can further optimize the plan continuously at kernel runtime. To eliminate LLM inference costs on the critical path, agentic kernel caches and reuses verified plans to bypass LLM entirely. Crucially, agentic kernel's safety guarantees are independent of LLM correctness: micro-syscalls follows syscall-level security mechanisms, ensuring OS safety under arbitrary combinations and orchestration. We implement agentic kernel in Linux, called IntentOS, and evaluate it across four kernel versions and three application domains (about ten intents each). IntentOS outperforms baseline Linux in real-world applications, achieving up to 17.55% latency reduction for backend processes creation, 6.97% performance gain for multiple files operations, and 75.52% lower performance impact for frontend Nginx server during memory operations triggered by backend tasks.

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