Siemens recently expanded its strategic partnership with Nvidia to add new AI capabilities to the Fuse EDA AI Agent, enabling self-verifying agentic AI workflows for semiconductor and printed-circuit board (PCB) design. The goal is to move toward agents that can remain active throughout long-running semiconductor and PCB design workflows. The new capability combines Siemens' electronic design automation (EDA) software with Nvidia's AI open models, agent software, secure runtime, and accelerated computing stack. It also spans Siemens' EDA technologies, including Catapult high-level synthesis, custom IC design and verification with Solido, Aprisa physical implementation, Calibre signoff verification, and Tessen design-for-test. The Fuse EDA AI Agent system, now integrated into Siemens' recently launched Intelligence Center X, supports agent creation and orchestration in its enterprise industrial AI environment. The self-verifying AI workflows enable autonomous and long-running EDA AI agents to continuously validate their decisions against proven engineering tools. It allows the agent to monitor a workflow, inspect the results, change its configuration, and run other operations while checking its progress against established EDA engines. This is what Siemens calls self-verification. The language model does not, by itself, determine whether a design is correct. Instead, the agent uses simulation, verification, and physical design tools to produce the engineering evidence for the next decision. For example, library characterization using Siemens' Solido Characterization Suite with agentic AI can reduce characterization turnaround by more than 10× and token cost by 5× to 10×, according to the company. These agentic workflows automatically generate and verify Liberty files via Solido Characterizer with Solido LibSPICE, Solido Generator, and Solido Analytics. Figure 1: Siemens' Fuse EDA AI platform roadmap from a foundational AI architecture in 2025 to end-to-end orchestration and self-verifying, long-running agents in 2026. (Source: Siemens) The journey toward self-verifying agents At DAC 2025, Siemens introduced the Fuse EDA AI System, which provided the data and model infrastructure to use generative AI with EDA data. The Fuse EDA AI Agent, announced at Nvidia GTC in March 2026, added an execution layer. The supervisory agent can break an engineering objective into tasks, delegate work to specialized agents, and connect to EDA applications through Model Context Protocol (MCP) interfaces. This new expansion, released at DAC 2026, adds long-running agents that can keep working after the first tool call. For example, an agent can launch a simulation, inspect the returned logs, identify a failure, modify the setup, and try again. The Fuse EDA AI Agent translates an engineer's objective into a plan and the specialized agents select tools and execute the steps. Extending the agentic AI workflows with Nvidia's Nemotron 3 Ultra model provides reasoning for planning and multi-step execution, while the Nvidia NeMo Gym open library is used to evaluate agent behavior to improve results and optimize workflows. It also leverages Nvidia OpenShell secure runtime to provide a secure environment for running the agents. But the engineering result comes from Siemens' EDA tools. For instance, a model may decide which SPICE analysis to run or if another process, voltage, and temperature (PVT) corner is needed, but the simulator produces the electrical result. "The goal is not to replace physics with AI," said Timothy Costa, general manager for industrial and computational engineering at Nvidia. The objective is to connect AI reasoning with accelerated simulation and physics-aware models and keep trusted engineering tools as the source of validation, he added. How a long-running agent handles library characterization Library characterization shows why this type of agent can be useful even when the underlying simulations are already automated. In the case