This topic examines modern autonomous-vehicle AI stacks that combine mission-grade autonomy software, agent frameworks, and AI data platforms with next‑generation NVIDIA Rubin-class chips to deliver low-latency, safety-focused perception and decision-making at the edge. Relevance in early 2026 stems from increased deployment pressures — expanding edge compute on vehicles and drones, tighter safety/regulatory expectations, and a shift toward on-device multimodal models and continuous fleet learning. Key categories: Edge AI Vision Platforms (real-time sensor fusion, optimized model runtimes, deterministic inference) and AI Data Platforms (labeling, simulation-to-reality data pipelines, validation and retraining). Representative tools: Shield AI’s Hivemind and EdgeOS provide deterministic middleware, behavior libraries (Pilot), and autonomy production tooling (Forge) for mission-critical systems; LangChain supplies engineering frameworks and stateful runtimes (LangGraph) to build and evaluate agentic workflows; AutoGPT-style platforms enable instantiation, orchestration, and lifecycle management of autonomous agents and automation workflows; Windsurf (formerly Codeium) is an AI-native IDE that accelerates development of agentic code, multi-model integration, and live testing. Practical considerations include hardware–software co‑design (optimized runtimes and compilers for Rubin-class accelerators), latency and power budgets for on-vehicle inference, verifiable behavior stacks for certification, simulation-to-reality validation, and robust data pipelines for continual model updates. Evaluations should weigh real-time perception accuracy, deterministic behavior guarantees, toolchain maturity, integration with fleet data platforms, and the degree of hardware partnership (driver/SDK support, precompiled kernels). This synthesis helps teams choose stacks that balance performance, safety, and operational lifecycle needs when deploying autonomy on Rubin-backed edge hardware.