Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents
NVIDIA demonstrated an AI coding agent-assisted workflow for building real-time endoscopic tool segmentation applications using Holoscan and HoloHub The iterative development approach decomposes complex goals into smaller, verifiable engineering iterations guided by engineer-defined objectives and constraints An ablation study showed that combining Holoscan CLI, development skills, and documentation/examples produced the most efficient and highest-quality development workflow compared to using d
Analysis
TL;DR
- NVIDIA demonstrated an AI coding agent-assisted workflow for building real-time endoscopic tool segmentation applications using Holoscan and HoloHub
- The iterative development approach decomposes complex goals into smaller, verifiable engineering iterations guided by engineer-defined objectives and constraints
- An ablation study showed that combining Holoscan CLI, development skills, and documentation/examples produced the most efficient and highest-quality development workflow compared to using documentation or CLI alone
- The agent-agnostic workflow was tested with Codex using GPT-5.6 sol max mode, completing the first iteration in approximately 40 minutes of agent processing time
- The Holoscan CLI provides a shared execution interface that both the AI agent and human engineer can use, enabling inspection and repeatability of development operations
Why It Matters
This represents a significant step toward practical AI-assisted software development for specialized edge AI platforms, demonstrating that general-purpose coding agents can effectively navigate complex SDK ecosystems when provided with the right tools and documentation. For AI practitioners and edge computing developers, this workflow pattern could dramatically reduce the time and expertise required to build production-grade real-time AI applications in domains like medical imaging and robotics.
Technical Details
- Platform: NVIDIA Holoscan for real-time edge AI applications, with HoloHub as the companion repository containing reference applications and components
- Development Tools: Holoscan CLI (invoked via
./holohubwrapper), HoloHub development skills (holohub-app-lifecycle,holohub-debug-build-run), and progressive disclosure documentation viaagents.md - Application Target: Real-time endoscopic tool segmentation reusing an existing MONAI model, with added HoloViz overlay for mask visualization, runtime telemetry, and repeatable benchmarking
- Workflow Architecture: Engineer defines goals and constraints → AI coding agent inspects examples and documentation → Agent implements code and runs CLI operations → Engineer reviews outputs and sets next iteration goals
- Benchmarking Approach: The agent inspected reference applications (
monai_endoscopic_tool_seg,endoscopy_tool_tracking,surgical_scene_recon), generated scaffolding via CLI, implemented the application graph using existing Holoscan operators, and connected video replay, preprocessing, TensorRT inference, segmentation postprocessing, telemetry, and visualization
Industry Insight
- The agent-agnostic nature of this workflow suggests that organizations can adopt AI coding assistants across diverse development teams without being locked into a single agent platform, enabling gradual integration of agentic tools into existing engineering pipelines
- The ablation study findings reinforce the importance of providing AI agents with structured, tool-accessible documentation and executable interfaces rather than raw text documentation alone, which should inform how SDK vendors design their developer experience and agent support
- The iterative decomposition strategy—breaking complex applications into reviewable, evidence-guided iterations—offers a practical template for managing AI-assisted development risk, ensuring human oversight at each stage while leveraging agent speed for implementation
Disclaimer: The above content is generated by AI and is for reference only.