These AI engineers are based on NVIDIA NemoClaw<\/a>, an open blueprint for building specialized, long-running agents with a secure runtime and frontier models.\u00a0<\/p>\n NemoClaw includes a choice of harness \u2014 meaning it can be integrated with various orchestration frameworks enterprises use to deploy and coordinate agents, such as OpenClaw and Hermes \u2014 as well as a model router and NVIDIA NeMo<\/a> libraries for customization.\u00a0<\/p>\n Users can easily deploy NemoClaw from NVIDIA DGX Spark<\/a> personal AI supercomputers, as well as through enterprise data centers and cloud service providers. NVIDIA OpenShell<\/a> \u2014 the open source runtime at its core \u2014 governs how each agent accesses files, networks and tools, enforcing policy-based security at every layer.<\/p>\n Industrial Engineering Leaders Build AI Agents Across Design, Engineering, Simulation<\/p>\n Industrial software leaders are building AI engineers for computer-aided engineering (CAE) and electronic design automation (EDA) use cases across automotive, aerospace, semiconductors and manufacturing.<\/p>\n Cadence<\/a> is building an autonomous register-transfer level (RTL) engineer with NemoClaw that orchestrates Cadence Design Systems ChipStack for design and verification. The workflow was featured yesterday in a GTC Taipei keynote demo and is cutting time for RTL verification \u2014 a key step in digital circuit design \u2014 from weeks to hours.<\/p>\n Dassault Syst\u00e8mes<\/a> is actively productizing the 3DEXPERIENCE Agentic Platform to operate long-running and autonomous agents for design, simulation and manufacturing operations, in a secured environment powered by NVIDIA NemoClaw and OpenShell.\u00a0\u00a0<\/p>\n Siemens<\/a> is integrating NVIDIA NemoClaw and OpenShell into Fuse EDA AI Agent, a purpose-built autonomous agent that plans and orchestrates domain-scoped multi-tool workflows across semiconductor, 3D integrated circuit and printed circuit board system design.<\/p>\n Synopsys<\/a> is collaborating with NVIDIA to apply agents to end-to-end engineering workflows with NVIDIA NemoClaw. Ansys Icepak, part of the Synopsys portfolio, is being demoed on the COMPUTEX show floor this week, used within a NemoClaw-based autonomous AI engineer to mesh, simulate and optimize GPU electronics cooling designs.<\/p>\n Image courtesy of Synopsys.<\/p>\n Startups Extend the Reach of Agentic AI<\/p>\n In addition, cutting-edge startups are building AI engineers for their workflows \u2014 all using NVIDIA NemoClaw.<\/p>\n Flexcompute is applying OpenShell to its Tidy3D and PhotonForge agents for multiphysics co-packaged optics design. Flexcompute\u2019s autonomous AI workflow combines optical, electrical and thermal simulation to explore thousands of design variants overnight, producing higher-performing components with lower energy consumption. NVIDIA is using Flexcompute technology for the design and optimization of advanced optical and photonic devices.<\/p>\n \u00a0<\/p>\n Video courtesy of Flexcompute.<\/p>\n Luminary is building a long-running AI engineer using NemoClaw to dramatically reduce the time and complexity of training AI physics models by autonomously orchestrating data generation, machine learning model selection, and training and re-training loops.<\/p>\n \u00a0<\/p>\n Video courtesy of Luminary.<\/p>\n Neural Concept is deploying an agent for electric motor design. The workflow chains electromagnetic, structural and noise, vibration and harness simulations in a multistep engineering pipeline.\u00a0<\/p>\n \u00a0<\/p>\n Video courtesy of Neural Concept.<\/p>\n nTop<\/a>, the geometry engine behind JetZero\u2019s blended-wing-body aircraft program, is using NVIDIA NemoClaw to run autonomous design workflows that compress days of geometry iteration into hours.<\/p>\n \u00a0<\/p>\n Video courtesy of nTop.<\/p>\n PhysicsX is partnering with the Microsoft Surface team to build an electronics thermal simulation agent that compresses weeks of manual CAE workflows into automated, AI-driven design cycles. Bringing together the PhysicsX platform, Microsoft Discovery and NVIDIA NemoClaw, the agent automates the full thermal simulation lifecycle for consumer devices such as Microsoft Surface laptops \u2014 from mesh sensitivity analysis and simulation data generation, through physics AI model training and optimization-loop execution, to continuous accuracy monitoring across the design exploration process.<\/p>\n \u00a0<\/p>\n Video courtesy of PhysicsX.<\/p>\n P-1 AI<\/a> is building Archie, an AI mechanical and electrical engineer that already works with data center cooling and critical power systems, and will soon work for automotive, aerospace and national security use cases. In a workflow representative of its work with Daikin Applied Americas, Archie synthesizes requirements, selects components, runs design trade studies and produces engineering artifacts to help industrial manufacturers scale engineering capacity.<\/p>\n \u00a0<\/p>\n Video courtesy of P-1 AI.<\/p>\n SimScale is adopting NVIDIA NemoClaw to build autonomous simulation agents for hundreds of cross-industry engineering use cases, including noise, vibration and harshness analysis, automating workflows that previously required multiple engineers working over several weeks.\u00a0<\/p>\n \u00a0<\/p>\n Video courtesy of SimScale.<\/p>\n Synera<\/a> is building an engineering agent for injection molding \u2014 a manufacturing process used to efficiently mass-produce identical parts by injecting molten material, usually plastic, into a custom mold \u2014 with Autodesk Moldflow, NVIDIA OpenShell with OpenClaw, as well as Nemotron models. <\/p>\n \u00a0<\/p>\n Video courtesy of Synera.<\/p>\n Learn more about NVIDIA technologies for CAE<\/a> and watch NVIDIA founder and CEO Jensen Huang\u2019s GTC Taipei keynote in replay<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Accelerated computing has revolutionized industrial engineering, compressing simulation times from weeks to hours.\u00a0 Today\u2019s remaining challenges sit in…\n","protected":false},"author":2,"featured_media":59852,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,405,3069,25,7537,34599,7034,335,34600],"class_list":["post-59851","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-ai-agents","tag-ai-training","tag-artificial-intelligence","tag-artificial-intelligence-agents","tag-customer-stories","tag-industrial-and-manufacturing","tag-open-source","tag-simulation-and-design"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/59851","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=59851"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/59851\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/59852"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=59851"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=59851"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=59851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/06\/synopsys-image-1680x1009.jpg\")
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