Governments and organizations alike are feeling increasing pressure to deliver value with agentic AI while maintaining data sovereignty, security, and regulatory compliance. The move to self-managed environments offers all of the above, but it also introduces new complexities that require a fundamentally new approach to AI stack design, especially in high-security environments.
Managing AI infrastructure means shouldering the full burden of integration, verification, and compliance. Every model, component and deployment must be inspected and tested. Even small updates can lead to rework, slow progress, and cause risk. In high-assurance environments, there is added weight to doing all of this with stringent regulatory and data sovereignty requirements.
What is needed is an AI stack that provides flexibility and assurance in local environments, enabling full lifecycle management wherever agent AI is deployed.
In this post, we’ll take a look at what it takes to deliver the agented workforce of the future even in the most secure and highly regulated environments, the risks of making mistakes, and how DataRobot and NVIDIA have come together to solve this problem.
With what was recently announced Agent workforce platform and NVIDIA AI Factory for Government With the reference design, organizations can now deploy agentic AI anywhere, from commercial clouds to sovereign air-sealed facilities, with secure access to NVIDIA Nemotron thinking models and full lifecycle control.
Fit-for-purpose agentic AI in secure environments
No two environments are the same when it comes to building an effective AI stack. In air-gap, sovereign, or mission-critical environments, every component, from hardware to model, must be designed and validated for interoperability, compliance, and observability.
Without this foundation, projects stall as teams spend months testing, integrating, and re-validating tools. Budgets expand while timelines slide, and the package grows more complex with each new addition. Teams often end up choosing between tools they’ve had time to examine, rather than between tools that best suit the task.
The result is a system that not only does not meet business needs, but simply maintaining and updating components can significantly slow down operations.
Starting with validated components and composable design addresses these challenges by ensuring that every layer—from accelerating infrastructure to development environments to agent AI in production—operates securely and reliably as a single system.
DataRobot and NVIDIA certified solution
DataRobot and Nvidia We have shown what is possible by offering a complete and fully proven solution for agent AI. Earlier this year, we introduced DataRobot Agent workforce platforma first-of-its-kind solution that enables organizations to build, operate and manage their own agent workforce.
Developed in collaboration with NVIDIA, this solution can be deployed in on-premises and even air-tight environments, and is fully validated for NVIDIA Enterprise AI Factory For the government reference structure. This collaboration gives organizations a proven foundation to develop, deploy and manage their AI workforce across any environment with confidence and control.
This means flexibility and choice at every layer of the stack, and every component that goes into agent AI solutions. IT teams can start with their own unique infrastructure and choose the components that best fit their needs. Developers can bring the latest tools and models to where their data resides, and quickly test, develop, and deploy them where they can provide the most impact while ensuring security and regulatory rigor.
Using DataRobot Workbench and registry, users can access… Nvidia NEM microservices With over 80 NIM, pre-built templates and development utilities that speed up and optimize your prototyping process. Trace tables and a visual tracking interface make it easy to compare at the component level and then tune the performance of the entire workflow before agents move to production.
With easy access to NVIDIA Nemotron inference models, organizations can deliver a flexible, intelligent workforce wherever they’re needed. Nvidia Nemotron models Combining NVIDIA’s full engineering expertise with truly open source access, enabling organizations to build, integrate and evolve agentic AI in ways that drive rapid innovation and impact across diverse missions and industries.
When agents are ready, organizations can deploy and monitor them with just a few clicks — integrating with existing CI/CD pipelines, applying guardrails for real-time oversight, and verifying compliance before going live.
NVIDIA AI Factory for Government provides a reliable foundation for DataRobot with a complete, integrated reference design that brings the power of AI to highly regulated organizations. Together, Agent Workforce Platform and NVIDIA AI Factory deliver the most comprehensive solution for building, operating, and managing intelligent agent AI on-premises, at the edge, and in the most secure environments.
Real-world agent AI at the edge: the Radio Intelligence Agent (RIA)
Deepwave, DataRobot and NVIDIA have brought this proven solution to life with Radio intelligence agent (Ria). This combined solution allows radio frequency (RF) signals to be converted into complex analysis – simply by asking a question.
Deepwave’s AIR-T sensors capture radio frequency (RF) signals and process them locally, eliminating the need to transmit sensitive data off-site. NVIDIA’s accelerated compute infrastructure and NIM microservices provide the secure inference layer, while NVIDIA Nemotron logic models interpret complex patterns and generate mission-ready insights.
DataRobot’s Agent Workforce Platform orchestrates and manages the lifecycle of these agents, ensuring that every model and microservice is deployed, monitored, and audited with complete control. The result is a sovereign-ready RF intelligence agent that provides continuous, proactive awareness and rapid decision support at the edge.
This same design can be adapted across use cases such as predictive maintenance, financial stress testing, cyber defense, and smart grid operations. Here are some applications of high security proxy systems:
| Industry and energy (edge/inside area) |
Federal and secure environments | Financial services |
| Pipeline fault detection and predictive maintenance | Signal intelligence processing for secure communications monitoring | Cutting-edge trading research |
| Monitor oil rig operations and safety compliance | Analyzing classified data in air-gapped environments | Recording credit risk by establishing controlled data |
| Detect critical faults in the infrared smart grid and ensure reliability | Secure battlefield logistics and improve the supply chain | Anti-Money Laundering (AML) with sovereign data processing |
| Monitor the health of mining site equipment remotely | Cyber defense and intrusion detection in blocked networks | Stress testing and scenario modeling under compliance controls |
The AI agent is designed for this task
Successfully deploying agentic AI in highly secure environments means going beyond balancing innovation and control. This means efficiently delivering the right solution for the job, where it’s needed, and keeping it running at the highest performance standards. This means expanding from a single agent solution to an agent workforce with complete visibility and trust.
When every component, from infrastructure to orchestration, works together, organizations gain the agility and assurance needed to deliver value from agentic AI, whether in a single edge solution or in an entire self-managed agentic AI workforce.
With NVIDIA AI Factory for Government providing the trusted foundation and DataRobot Agent workforce platform With coordination and control, organizations and agencies can deploy agentic AI anywhere with confidence, and scale securely, efficiently, and with complete visibility.
To learn more about how DataRobot can help fuel your AI ambitions, visit us at datarobot.com/government.
