NVIDIA and ServiceNow Partner on New Autonomous AI Agents for Enterprises

<div id="bsf_rt_marker"></div><p><span style="font-weight: 400;">Enterprise AI has learned to generate. It has learned to reason. Now companies are asking the next question: How should AI act?</span></p> <p><span style="font-weight: 400;">Early agent systems have shown what’s possible, moving beyond simple prompts to take on more complex tasks. The next step is bringing those capabilities into enterprise environments — where agents must operate with context, control and consistency across real workflows.</span></p> <p><span style="font-weight: 400;">At ServiceNow Knowledge 2026, NVIDIA founder and CEO Jensen Huang joined ServiceNow chairman and CEO Bill McDermott during the opening keynote to discuss the next phase of enterprise AI.</span><span style="font-weight: 400;"> </span></p> <p><span style="font-weight: 400;">The companies are expanding their collaboration across the full stack, delivering specialized autonomous AI agents that are safe and easy to adopt — powered by NVIDIA accelerated computing, open models, domain-specific skills and secure agent execution software, and bringing together enterprise workflow context from ServiceNow Action Fabric and governance from ServiceNow AI Control Tower.</span></p> <p><span style="font-weight: 400;">ServiceNow is introducing <a target="_blank" href="https://newsroom.servicenow.com/press-releases/details/2026/ServiceNow-extends-agentic-AI-governance-from-desktops-to-data-centers-with-NVIDIA/default.aspx">Project Arc</a>, a long-running, self-evolving autonomous desktop agent designed for knowledge workers, including developers, IT teams and administrators. </span></p> <p><span style="font-weight: 400;">Unlike standalone AI agents, Project Arc connects natively to the ServiceNow AI Platform through ServiceNow Action Fabric to bring governance, auditability and workflow intelligence to every action the autonomous desktop agent takes. It can access the local file systems, terminals and applications installed on a machine to complete complex, multistep tasks that traditional automation can’t handle, but with the controls enterprises actually need to deploy AI at scale.</span></p> <p><span style="font-weight: 400;">The work is designed based on three requirements every company will need for long-running, autonomous agents: open models and domain-specific skills that can be customized and security that helps agents act without exposing sensitive data or systems — all running on AI factories that deliver efficient </span><a href="https://blogs.nvidia.com/blog/ai-tokens-explained/"><span style="font-weight: 400;">tokenomics</span></a><span style="font-weight: 400;">.</span></p> <p><span style="font-weight: 400;">Bringing this level of autonomy to enterprises requires control from the start.</span></p> <p><span style="font-weight: 400;">Project Arc uses </span><a target="_blank" href="https://build.nvidia.com/openshell"><span style="font-weight: 400;">NVIDIA OpenShell</span></a><span style="font-weight: 400;">, an open source secure runtime for developing and deploying autonomous agents in sandboxed, policy-governed environments. ServiceNow is building on and contributing to OpenShell to advance a common foundation for secure, enterprise-grade agent execution. With OpenShell, enterprises can define what an agent can see, which tools it can use and how each action is contained. </span></p> <p><span style="font-weight: 400;">“Project Arc represents the next step in our ongoing collaboration with NVIDIA, bringing autonomous execution to the desktop,” </span><span style="font-weight: 400;">said Jon Sigler, executive vice president and general manager of AI Platform at ServiceNow.</span><span style="font-weight: 400;"> “By combining OpenShell’s runtime layer with ServiceNow AI Control Tower, and powered by ServiceNow Action Fabric, we’re delivering the governance and security that enterprise AI requires.”</span><span style="font-weight: 400;"> </span></p> <h2><b>Open Models and Agent Skills Scale Enterprise AI</b></h2> <p><span style="font-weight: 400;">To be effective, enterprise AI systems must be adaptable. NVIDIA and ServiceNow are building on an open ecosystem that allows organizations to tailor models and applications to their specific domains and data.</span></p> <p><span style="font-weight: 400;">NVIDIA agent skills enable specialized agents, such as ServiceNow AI Specialists, to deliver targeted capabilities across enterprise workflows. For example, the </span><a target="_blank" href="https://build.nvidia.com/nvidia/aiq"><span style="font-weight: 400;">NVIDIA AI-Q Blueprint</span></a><span style="font-weight: 400;"> for building specialized deep research agents empowers ServiceNow AI Specialists to gather context, synthesize information and support more complex decision-making across business functions. </span></p> <p><span style="font-weight: 400;">In addition, the NVIDIA Agent Toolkit, including </span><a target="_blank" href="https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/"><span style="font-weight: 400;">NVIDIA Nemotron</span></a><span style="font-weight: 400;"> open models, provide flexible building blocks and specialized skills for developing customized AI applications. To support real-world performance that these systems can perform reliably, the companies are also advancing NOWAI-Bench, an open benchmarking suite for enterprise AI agents, integrated with the NVIDIA NeMo Gym library. NOWAI-Bench includes </span><a target="_blank" href="https://enterpriseops-gym.github.io/"><span style="font-weight: 400;">EnterpriseOps-Gym</span></a><span style="font-weight: 400;">, one of the industry’s most challenging enterprise agent benchmarks, where Nemotron 3 Super currently ranks No. 1 among open source models.</span></p> <p><span style="font-weight: 400;">Unlike general benchmarks, these evaluations focus on multistep workflows — where enterprise AI systems often encounter real challenges — helping teams build agents that perform reliably in production environments.</span></p> <h2><b>Efficient AI Factories</b></h2> <p><span style="font-weight: 400;">As AI agents become long running and always on, scaling them across millions of workflows requires not just capability but efficiency — making token economics central to enterprise AI.</span></p> <p><span style="font-weight: 400;">NVIDIA AI factories are built to deliver the lowest-cost, most-efficient tokenomics for production AI. The </span><a target="_blank" href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/"><span style="font-weight: 400;">NVIDIA Blackwell</span></a><span style="font-weight: 400;"> platform delivers more than 50x greater token output per watt than NVIDIA Hopper, resulting in nearly 35x lower cost per million tokens. For enterprises running agents across millions of workflows, that efficiency can determine how quickly AI moves from pilots to broad production use.</span></p> <p><span style="font-weight: 400;">ServiceNow AI Control Tower integrates with the </span><a target="_blank" href="https://www.nvidia.com/en-us/solutions/ai-factories/validated-design/"><span style="font-weight: 400;">NVIDIA Enterprise AI Factory</span></a><span style="font-weight: 400;"> validated design, extending governance and observability to large-scale AI workloads. With added agent observability capabilities, organizations can monitor behavior in real time and manage AI systems across their full lifecycle — from deployment to optimization.</span></p> <p><span style="font-weight: 400;">AI is becoming a new way that work gets done. What’s changing now is that the core pieces required to deploy it at scale — capable agents, built-in guardrails and proven performance — are all coming together.</span></p> <p><span style="font-weight: 400;">The companies that move fastest will be the ones that give agents the infrastructure to act, the context to make decisions and the governance to keep every action accountable — and NVIDIA and ServiceNow are making this a reality for the world’s enterprises.</span></p> <p><i><span style="font-weight: 400;">Learn more about</span></i> <a target="_blank" href="https://build.nvidia.com/openshell"><i><span style="font-weight: 400;">NVIDIA OpenShell</span></i></a><i><span style="font-weight: 400;"> and the</span></i> <a target="_blank" href="https://build.nvidia.com/nvidia/aiq"><i><span style="font-weight: 400;">NVIDIA AI-Q Blueprint</span></i></a><i><span style="font-weight: 400;">. </span></i></p>