eBPF lets developers extend Linux with custom packet processing, tracing, and scheduling logic, and a verifier proves before execution that the code will not crash the kernel. The programming model, however, is fragmented: a single application spans kernel code, a userspace loader, and shared maps, yet the relationships among these pieces go unchecked. E.g. A map or event type defined differently on each side silently corrupts shared state. We observe that these cross-boundary relationships duplicate information that a type system can unify. We present KernelScript, a DSL that types maps, program handles, and execution domains in one source, then compiles to standard C through the original toolchain. We evaluate KernelScript on 43 eBPF workloads covering XDP, TC, kprobe, tracepoint, and struct_ops. KernelScript rejects cross-boundary bugs at compile time that standard C/libbpf still builds and loads, a unified source shrinks the diffs for cross-boundary changes by 5x, and generated code remains compatible with the existing toolchain.
In recent years, AI agents have evolved into capable assistants that carry out multi-step tasks in digital environments. The network systems community is beginning to explore these capabilities in operational and experimental settings. However, an agent operating in network systems should not be judged solely by whether it completes the immediate task. The experiment record it modifies must also remain trustworthy. We call this property artifact integrity: the record's claims must remain supported by the available evidence, confined to the scope established by that evidence, and traceable through the artifacts that encode their support. To make this property measurable, we introduce NetArtifactBench, which tests whether AI agents can repair inconsistent records derived from public network-system artifacts while preserving claims that remain supported. The benchmark contains 52 instances with injected inconsistencies ranging from direct contradictions to unstated relations spread across several artifacts. We evaluate 23 agent configurations across three general-purpose AI agent runtimes using deterministic scoring. The average contract pass rate is 65.3 % across 5,980 outputs, but no agent runtime exceeds 30 % when repair requires recovering implicit relations and propagating changes across artifacts. These results reveal a sharp boundary between local correction and complete record-level repair. Therefore, we argue that artifact integrity should become a first-class design and evaluation requirement for AI agents operating on network systems.
Tianzhu Zhang, Wei-Chen Tao, Chang-Gang Zheng et al.· 0 citations
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