Summary
NVIDIA’s reference architecture combines isolated agent runtimes, infrastructure, and monitoring for governed enterprise and edge deployments.
About
A full-stack architecture for organizations seeking to deploy AI agents with runtime isolation, monitoring, and policy controls across enterprise and edge environments.
NVIDIA OpenShell Reference Architecture is a design for building, containing, observing, and controlling AI agents across enterprise infrastructure and edge systems. It combines OpenShell secure runtime software with agent infrastructure, open and closed models, and NVIDIA Sentry monitoring and policy enforcement. The architecture is intended for organizations deploying agents at scale that require governance and visibility across software and hardware. NVIDIA announced it as available in October 2026.
Key Features
Uses OpenShell secure runtime software to contain AI agents.
Combines agent infrastructure with open and closed models.
Includes NVIDIA Sentry for monitoring and policy enforcement.
Supports deployments across enterprise infrastructure and edge systems.
Provides a design for observing and governing agents across software and hardware.
Real-World Use Cases
Governed enterprise agent deployments
Use the architecture as a foundation for deploying agents at scale with runtime containment, monitoring, and policy enforcement.
Edge agent operations
Apply the architecture to agent deployments on edge systems where organizations need visibility and controls across software and hardware.
Agent infrastructure planning
Use the reference design to plan how agent runtimes, infrastructure, models, monitoring, and enforcement fit together.
Business Opportunities
Enterprise agent governance implementation
Problem: Organizations deploying agents at scale may need consistent containment, monitoring, and policy enforcement across their infrastructure.
Solution: Use the reference architecture to guide the integration of secure runtimes, agent infrastructure, models, and NVIDIA Sentry controls.
Estimated ROI: Unknown. Potential value may come from improved governance and operational visibility, but the provided information does not establish costs, measured outcomes, or savings.
Execution: Assess agent workloads and infrastructure, map governance requirements to the architecture, and validate the design in a limited deployment before expanding.
Edge agent deployment design
Problem: Organizations operating agents across edge systems may lack a unified approach to runtime controls and visibility across software and hardware.
Solution: Use the architecture as a design reference for combining agent runtimes, infrastructure, monitoring, and policy enforcement in edge deployments.
Estimated ROI: Unknown. The context provides no quantified deployment costs or business outcomes.
Execution: Identify edge environments and operational constraints, adapt the reference design to the deployment, and test monitoring and enforcement requirements before broader rollout.