At Nvidia GTC 2026, Jensen Huang, CEO of Nvidia, introduced NemoClaw, a new stack for the OpenClaw agent platform designed to help developers and organizations deploy autonomous AI agents with built-in security, privacy, and governance controls.
The announcement reflects a broader shift in the artificial intelligence industry toward agentic AI systems, software agents capable of performing complex tasks across applications, APIs, and digital environments.
During his keynote, Huang highlighted the rapid rise of the OpenClaw ecosystem, which has gained widespread attention in the developer community.
"OpenClaw opened the next frontier of AI to everyone and became the fastest-growing open source project in history."
"Mac and Windows are the operating systems for the personal computer. OpenClaw is the operating system for personal AI."
Image source: Envato
A security and governance layer for AI agents
Nvidia designed NemoClaw to address one of the biggest emerging challenges in the agentic AI ecosystem: how to safely run autonomous agents that interact with real systems, data, and networks.
The NemoClaw stack introduces several capabilities intended to give developers and organizations greater control over agent behavior, including:
- Secure execution environments that sandbox AI agent activity
- Policy-based governance controls that regulate what agents can access or execute
- Privacy and network guardrails to manage how agents communicate with external services
- Integration with Nvidia Agent Toolkit, which provides models, runtime components, skills, and development blueprints for building AI agents
Together, these tools aim to make it easier to experiment with autonomous agents while reducing risks related to data access, system permissions, and unintended actions.
OpenShell runtime powers the NemoClaw stack
A core component of NemoClaw is the OpenShell runtime, a runtime environment designed to run and orchestrate AI agents while enforcing security policies.
OpenShell manages how agents interact with:
- Operating systems
- Networks
- Files and APIs
- AI model inference services
The runtime can enforce declarative security policies, allowing organizations or developers to define what agents are allowed to do before they are deployed. This approach is intended to prevent common risks associated with autonomous agents, such as unrestricted system access or uncontrolled data flows.
Built for both developers and enterprises
According to Nvidia, NemoClaw is designed to run across multiple infrastructure environments, including:
- Cloud platforms
- On-premises systems
- Nvidia DGX infrastructure
- RTX-powered workstations and PCs
The stack also supports integration with Nvidia's broader AI ecosystem, including Nemotron models and other compatible AI models. While enterprise security is a major focus, Nvidia also positioned NemoClaw as a platform that individual developers can use to build always-on personal AI assistants and autonomous workflows.
The rise of agentic AI platforms
The launch of NemoClaw highlights a growing trend across the AI industry: the development of platforms designed to manage networks of autonomous agents capable of performing multi-step tasks.
Major technology companies are increasingly investing in systems that allow AI agents to:
- Coordinate across software tools
- Execute complex workflows
- Interact with real-world digital infrastructure
As these capabilities expand, security, governance, and oversight are becoming critical requirements for large-scale adoption. With NemoClaw, Nvidia is positioning itself to provide the infrastructure needed to deploy and manage autonomous AI agents safely, as agentic systems move from experimentation toward real-world applications.
Leave A Comment