Super AI Agents for Science Manufacturing and Engineering GTC Berlin 2026 Edition

The most intelligent scientist still needs a laboratory. The best engineer still needs simulators and solvers. An AI agent is no different. An agent brings together five building blocks. The model perceives, reasons, and plans. The harness coordinates the work. Knowledge Foundation gives the agent context from your company's structured and unstructured data. Tools let the agent simulate, experiment, and act. NVIDIA Open Shell controls what agents can access and execute. Start with a frontier closed model or customize an open model. With NVIDIA's Agent Toolkit, any company can turn its knowledge and expertise into a customized super agent. Let's look at three examples. For Lila Sciences, the goal is to design a therapy for B-cell lymphoma. The Lila Iris agent, built on NVIDIA Nemotron, turns that goal into an experimental plan. In the computational dry lab, Lila agents use NVIDIA BioNeMo Agent Toolkit to call models for protein design and RNA optimization, running millions of virtual experiments. The most promising candidates move into Lila's AI science factory, where autonomous robots and instruments, developed with NVIDIA Omniverse and Cosmos, make and test them. The results return to IRIS, shaping the next designs and experiments. The loop continues. At Siemens Electronic Factory Erlangen, the goal is to bring new products into production. Process Preparation X Agents use NVIDIA Cosmos to generate virtual prototypes and synthetic data, then train and validate inspection models before production starts. Intelligence Center X Agents, built with NVIDIA's factory operations blueprint, use orders, supplies, and line setups to recommend production plans. Digital Twin Composer uses NVIDIA Omniverse libraries to validate those plans with physics simulation and real-time factory data before changes reach the factory floor. With the Dassault Systems platform, an engineer needs to adapt a Volkswagen Golf door design for the Tiguan. Aura and Leo use NVIDIA Nemotron to reason over engineering requirements. The harness coordinates the specialized sub-agents and preserves context across planning, design, and simulation. Aura uses Inovia to plan the work around the vehicle's dimensions, weight limits, and other engineering requirements. LEO Design Agent selects a compatible golf door design and uses CATIA to reshape it for the Tiguan. LEO Simulation Agent uses Simulia, accelerated by NVIDIA CUDA-X libraries, to test the variants, revealing a vibration problem. The results return to the Design Agent for refinement. The engineer reviews the refined designs and selects the final one. Different goals. Different expertise. The same building blocks. Open models. CUDA-X accelerated tools. And the Open Shell secure runtime. Brought together with the NVIDIA Agent Toolkit.