AI Native by Design: Lessons Learned from Building NVIDIA TensorRT Model Connect | NVIDIA Technical Blog

This post details the engineering lessons learned from building NVIDIA TensorRT Model Connect, an open-source project designed around AI agents. Key contributions include: 1. **AI-Native Design Principles:** Treating AI outputs as modular, verifiable units of work to prevent error cascades. 2. **Horizontal Scalability:** Selecting problems that can be decomposed into parallel tasks, exemplified by supporting 128 model families independently. 3. **Outcome-Based Agent Interaction:** Providing agents with high-level goals and objective references rather than prescriptive recipes, allowing them to explore implementation paths. 4. **Isolation as a Scaling Unit:** Separating components with different evolution speeds (TensorRT/CUDA foundation, TensorRT Model Connect integration layer, model-family implementations) to reduce coupling and error impact. 5. **Reversibility:** Prioritizing changes that are easy to evaluate and revert. 6. **Evidence-Centric Validation:** Emphasizing human-legible evidence, agent-native self-improving tests, reproducible CI, and adversarial collaboration between QA and developers, recognizing that as candidate code becomes cheaper, evidence becomes more expensive. The project shifts human judgment upstream to system design and accountability.