Autobrains and Uber to Launch Agentic AI Robotaxi Program in Munich built on NVIDIA DRIVE Hyperion Key Terms agentic ai technical robotaxi technical autonomous driving technical software-defined technical - Combining Uber’s mobility network, Autobrains’ Agentic AI, and NVIDIA DRIVE Hyperion - Launching a Munich robotaxi service designed to scale into a fully autonomous fleet - Creating an OEM-agnostic model for autonomous ride-hailing across vehicle platforms Pending regulatory approval, Autonomy has often depended on custom vehicles, heavy sensor stacks, and compute architectures that are much harder to commercialize at scale. Autobrains’ Agentic AI is designed to change that - enabling robust, real-time decision-making on standard automotive sensor configurations and efficient, accelerated compute, with the flexibility to deploy across OEM platforms. The collaboration combines three essential layers for scalable robotaxi deployment: Autobrains’ Agentic AI autonomous driving technology, the NVIDIA DRIVE Hyperion platform, and Uber’s global mobility network and operational experience. Together, they establish an OEM-agnostic model to move autonomous ride-hailing from isolated deployments to repeatable, scalable fleet infrastructure. Autobrains’ Agentic AI represents a new path to autonomy. Rather than relying on a single, monolithic end-to-end model to handle the full driving task, Autobrains decomposes driving into specialized agents, each focused on a specific driving context or decision dimension. These agents continuously evaluate context, reason across multiple possible actions, and select responses in real time. This enables more robust behavior in complex, unpredictable environments, while maintaining the efficiency needed to deploy across fleets and OEM platforms. The program is designed to integrate across vehicle platforms and operate within Uber’s ride-hailing ecosystem. For automakers, it creates a practical path to participate in autonomous ride-hailing by combining vehicle platforms with autonomous technology, marketplace access, and fleet operations. “Autonomous driving will not scale by relying on a single model to solve every driving scenario,” said Igal