How to build and scale your agent workforce with DataRobot and NVIDIA

Building an agent workforce is not the same as deploying a few agents. It is a strategic shift that determines how enterprises deliver value at scale.

The challenge is not just creating functional agents. It is ensuring they align with business goals, run on infrastructure you control, and meet strict security and compliance requirements.

For most organizations, that complexity can be overwhelming. Teams wrestle with stitching together fragmented tools, managing brittle integrations, and navigating governance gaps across sprawling systems.

Each new use case adds integration overhead, while scaling GPU infrastructure and meeting sovereignty requirements create additional pressure.

To make this work, enterprises need more than a collection of components. They need a unified approach that combines development, deployment, and governance in a platform designed for control and flexibility from day one.

This post explores how DataRobot and NVIDIA help you build and scale a governed, production-ready agent workforce.

What your agentic AI stack needs

Running agents in production isn’t just about building a workflow. It’s about making sure it scales, stays reliable, and meets compliance as usage grows. That requires more than a patchwork of tools. You need an end-to-end stack that brings development, deployment, and governance together in one system.

Building your agent workforce with DataRobot, powered by NVIDIA

DataRobot and NVIDIA deliver a co-engineered solution that combines high-performance infrastructure with a unified agent lifecycle platform. The result: faster builds, smoother deployments, and fewer manual steps to manage.

With DataRobot, you can:

• Jumpstart development with customizable agentic AI app templates that provide pre-built workflows tailored to common, high-impact business problems.
  
• Streamline deployment on managed infrastructure using built-in guardrails and native integrations for enterprise systems.
  
• Ensure enterprise-grade governance and observability with centralized asset tracking, built-in monitoring, and automated compliance reporting across any environment.
  
  

With NVIDIA AI Enterprise embedded into DataRobot, you can:

• Use performance-optimized AI model containers and enterprise-grade development software.
• Simplify deployment setup with NVIDIA NIM and NeMo microservices, that work out of the box.
• Pull deployed NIM models into the DataRobot playground and start building without configuration headaches.
• Accelerate collaboration across AI and DevOps teams to deploy agents quickly.
• Monitor and automatically improve all deployed agentic AI apps across environments.

10 steps to take agents from prototype to production

Follow this step-by-step process for using DataRobot and NVIDIA AI Enterprise to build, deploy, and scale agents quickly and efficiently.

Step 1: Browse NVIDIA NIM gallery and register in DataRobot 

Access a full library of NVIDIA NIM directly within the DataRobot Registry. These pre-tuned, pre-configured components are optimized for NVIDIA GPUs, giving you a high-performance foundation without manual setup.

When imported, DataRobot automatically applies versioning and tagging, so you can skip setup steps and get straight to building.

To get started:

1. Open the NVIDIA NIM gallery within DataRobot’s registry.
2. Select and import the model into your registry.
3. Let DataRobot handle the setup. It will recommend the best hardware configuration, allowing you to focus on testing and optimizing instead of troubleshooting infrastructure.

Step 2: Select a DataRobot app template

Start compiling and configuring your agentic AI app with pre-built, customizable templates that eliminate setup work and let you go straight into prototyping, testing, and validating.

The DataRobot app library provides frameworks designed for real-world deployment, helping you get up and running quickly. 

1. Select a template that best matches your use case.
2. Open a codespace, which comes pre-configured with setup instructions.
3. Customize your app to run on NVIDIA NIM and fine-tune it for your needs

Step 3: Open your NVIDIA NIM into DataRobot Workbench to build and optimize your VDB

With your app template in place and hardware selected, it’s time to bring in the generative AI component and start building your vector database (VDB) in the DataRobot Workbench.

1. Open your NVIDIA NIM in the DataRobot Workbench. A use case will be created automatically.
2. Connect your data and navigate to the Vector Databases tab.
3. Select data sources and choose from multiple embedding models. DataRobot will automatically recommend one and provide alternatives to test.
   
   You can also import embedding and reranking models from NVIDIA in DataRobot Registry and make them available with the VDB creation interface.
4. Build one or multiple VDBs to compare performance before integrating them into your RAG workflow in the next step. 

Step 4: Test and evaluate NVIDIA NIM LLM configurations in the LLM Playground

In DataRobot’s LLM Playground, you can quickly build, compare, and optimize different RAG workflows and LLM configurations without tedious manual switching.

Here’s how to test and refine your setup:

1. Create a Playground within your existing use case.
2. Select LLMs, prompting strategies, and VDBs to include in your test.
3. Configure up to three workflows at a time and run queries to compare performance.
4. Analyze results and refine your configuration to optimize response accuracy and efficiency.

Step 5: Add predictive elements to your agents

(If your app uses only generative AI, you can move on to packaging with guardrails and final testing.)

For agents that incorporate forecasting or predictive tasks, DataRobot streamlines the process with its built-in predictive AI capabilities.

DataRobot will automatically:

• Analyze the data, detect feature types, and preprocess it.
• Train and evaluate multiple models, ranking them with the best-performing one at the top.

Then you can:

• Analyze key drivers behind the prediction.
• Compare different models to fine-tune accuracy.
• Integrate the selected model directly into your agent.

Step 6: Add the right tools to your app 

Expand your app’s capabilities by integrating additional tools and agents, such as the NVIDIA AI Blueprint for video search and summarization (VSS), to process video feeds and transform them into structured datasets.

Here’s how to enhance your app:

• Create additional tools or agents using frameworks like LangChain, NVIDIA AgentIQ, NeMo microservices, NVIDIA Blueprints, or options from the DataRobot library.
• Expand your data sources by integrating hyperscaler-grade tools that work across cloud, self-managed, and bare-metal environments.
• Deploy and test your app to ensure seamless integration with your generative and predictive AI components.

Step 7: Add monitoring and safety guardrails 

Guardrails are your first line of defense against bad outputs, security risks, and compliance issues. They help ensure AI-generated responses are accurate, secure, and aligned with user intent. 

Here’s how to add guardrails to your app:

1. Open your model in the Model Workshop.
2. Click “Configure” and navigate to the Guardrails section.
3. Select and apply built-in protections such as NVIDIA NeMo Guardrails, including:
   
   Stay on Topic
   Content Safety
   Jailbreak
4. Customize thresholds or add additional guardrails to align with your app’s specific requirements.

Step 8: Design and test your app’s UX

A well-designed UX makes your AI app intuitive, valuable, and easy to use. With DataRobot, you can stage a complete version of your app and test it with end users before deployment.

Here’s how to test and refine your UX:

• Stage your app in DataRobot for testing.
• Share it via link or embed it in a real-world environment to gather user feedback.
• Gain full visibility into how the app works, including chain of thought reasoning for transparency.
• Incorporate user feedback early to refine the experience and reduce costly rework.

Step 9: Deploy your agents with one-click

With one-click deployment, you can instantly launch NVIDIA NIMs from the model registry without manual setup, tuning, or infrastructure management. 

Your app, guardrails, and monitoring are deployed together, ensuring full traceability and governance.

Here’s how to deploy:

1. Select the NVIDIA NIM model you want to use.
2. Choose your GPU configuration and set any necessary runtime options, all from a single screen.
3. Deploy with one click. DataRobot automatically packages and registers your model with all necessary components.

Step 10: Monitor and govern your deployment in DataRobot

After deployment, your agent requires continuous monitoring to ensure long-term stability, accuracy, and performance. NIM deployments use DataRobot’s observability framework to surface key metrics on health and usage.

The DataRobot Console provides a centralized view to:

• Track all AI applications in a single dashboard.
• Identify potential issues early before they impact performance.
• Drill down into individual prompts and deployments for deeper insights.

Break the iteration cycle

Complex AI projects stall when teams spend too much time swapping components, tuning combinations, and re-running tests to keep up with evolving requirements. Without clear visibility or structured workflows, teams can easily lose track of what’s working and waste time redoing the same steps.

Best practices to reduce friction and maintain momentum:

• Test and compare as you go. Experiment with different configurations early to avoid unnecessary rework. DataRobot’s LLM Playground makes this fast and simple.
• Use structured workflows. Stay organized as you test variations in components and configurations.
• Leverage audit logs and governance tools. Maintain full visibility into changes, streamline collaboration, and reduce duplication. DataRobot can also generate compliance documentation as part of the process.
• Swap components seamlessly. Use a modular platform that lets you plug and play without disrupting your app.

By adopting these practices, your team can move faster, stay aligned, and deliver production-ready agents without getting stuck in endless iteration.

Make your agents enterprise-ready

Agentic AI only creates impact when it runs reliably in production and scales without breaking trust.

DataRobot and NVIDIA AI Enterprise brings together speed, governance, and flexibility so you can build, deploy, and manage agents with confidence.

Whether you’re launching your first AI agent or scaling a portfolio of enterprise-grade solutions, this platform gives you the structure and reliability to turn innovation into real business results.

Ready to build? Book a demo with a DataRobot expert and see how fast you can go from prototype to production.