This demo is a side-by-side comparison of two AI coding agents tasked with building a Go-based RDMA application using NVIDIA DOCA on a BlueField-3 platform. Both agents are given the same goal: implement a working RDMA data path and verify that bytes successfully arrive on hardware. The key difference is that one agent has access to NVIDIA DOCA “skills,” while the other must discover the required modules, headers, and build process from scratch through iterative hardware probing. The experiment highlights how access to curated domain knowledge impacts efficiency: - The “skilled” agent rapidly identifies the correct DOCA modules, samples, and CGO build configuration. - The “unskilled” agent relies on trial-and-error, repeatedly probing hardware and discovering components incrementally. - Both approaches ultimately produce a working, hardware-verified RDMA application. Key results from the comparison: - ~73% reduction in handwritten code when using DOCA skills - ~50% fewer hardware interaction commands - Faster convergence to NVIDIA-supported sample-based implementation - More consistent use of validated DOCA pipelines instead of low-level reimplementation The demo illustrates how structured platform knowledge can significantly reduce development effort while maintaining correct and verifiable low-level hardware behavior on accelerated networking systems.

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