NVIDIA is taking a big swing at one of the toughest problems in modern AI: how to run powerful AI agents at the edge or inside company networks without creating a security and privacy nightmare. The company’s new initiative, called NemoClaw, is designed to make OpenClaw practical for enterprise use by adding stronger security layers and optimizing performance for real-world deployments.
OpenClaw has exploded in popularity because it delivers something many people have been waiting for—AI that feels genuinely useful day to day, not just impressive in demos. NVIDIA CEO Jensen Huang even positioned OpenClaw as a major turning point for computing, arguing that the industry is moving into an era of “agentic computing,” where software agents take on tasks autonomously, similar to how earlier platform shifts transformed personal computing. In his view, OpenClaw’s adoption has accelerated so rapidly that it has surpassed other major open-source success stories.
At GTC 2026, NVIDIA described how it worked with top security researchers to reshape OpenClaw for business environments, then reintroduced the enterprise-hardened version under the NemoClaw name. The goal is straightforward: keep the magic of OpenClaw’s agent-driven workflows, but make them safe to run where sensitive data lives.
Peter Steinberger, the founder of OpenClaw, emphasized the broader mission behind the project: bringing AI closer to everyday people and enabling a future where everyone can have their own AI agents—supported by the right “guardrails” so these assistants remain powerful without becoming risky.
So what actually changes with NemoClaw? NVIDIA says it enhances the original agent experience with an Agent Toolkit aimed at improving how agents communicate in enterprise deployments, helping organizations lock down interactions and reduce exposure. NemoClaw also uses OpenShell, which runs autonomous agents in an isolated sandbox designed to strengthen data privacy and security—an important detail for companies worried about agents touching confidential files, internal tools, or customer information.
Beyond protection, NemoClaw also benefits from NVIDIA’s wider open-source ecosystem. It can tap into tools and libraries such as cuDF, Nemotron Dynamo, cuOPT, and more. In practice, that means better performance, more capabilities, and a smoother path for developers who want agents that can handle complex work reliably.
NVIDIA is also pairing this with its latest agent-focused language model strategy. Jensen highlighted Nemotron 3 Super as an ideal model to deploy alongside OpenClaw-style agents. Nemotron 3 Super is an open-source large language model built for long-context workloads while keeping to a 120 billion parameter design. NVIDIA’s pitch is that combining Nemotron 3 Super with NemoClaw’s additional security layers significantly reduces one of the biggest barriers to edge AI agents: privacy. If enterprises can run capable agents with stronger isolation and tighter controls, they can finally deploy them closer to where the data and workflows actually are—without sending everything out to external services.
Taken together, NemoClaw is NVIDIA’s attempt to push AI agents beyond experimentation and into serious business adoption. By focusing on enterprise-grade security, privacy-first deployment, and a more optimized agent toolkit, NVIDIA is betting that the next wave of AI won’t just be about bigger models—it will be about trustworthy agents that can operate safely and effectively anywhere, including on the edge.
