Agentic AI with NVIDIA and DataRobot

Constructing an agent workforce will not be the identical as deploying a number of brokers. It’s a strategic shift that determines how enterprises ship worth at scale.

The problem is not only creating purposeful brokers. It’s making certain they align with enterprise objectives, run on infrastructure you management, and meet strict safety and compliance necessities.

For many organizations, that complexity might be overwhelming. Groups wrestle with stitching collectively fragmented instruments, managing brittle integrations, and navigating governance gaps throughout sprawling programs.

Every new use case provides integration overhead, whereas scaling GPU infrastructure and assembly sovereignty necessities create further strain.

To make this work, enterprises want greater than a set of parts. They want a unified strategy that mixes improvement, deployment, and governance in a platform designed for management and suppleness from day one.

This submit explores how DataRobot and NVIDIA make it easier to construct and scale a ruled, production-ready agent workforce.

What your agentic AI stack wants

Working brokers in manufacturing isn’t nearly constructing a workflow. It’s about ensuring it scales, stays dependable, and meets compliance as utilization grows. That requires greater than a patchwork of instruments. You want an end-to-end stack that brings improvement, deployment, and governance collectively in a single system.

Constructing your agent workforce with DataRobot, powered by NVIDIA

DataRobot and NVIDIA ship a co-engineered resolution that mixes high-performance infrastructure with a unified agent lifecycle platform. The end result: sooner builds, smoother deployments, and fewer guide steps to handle.

With DataRobot, you may:

• Jumpstart improvement with customizable agentic AI app templates that present pre-built workflows tailor-made to widespread, high-impact enterprise issues.
• Streamline deployment on managed infrastructure utilizing built-in guardrails and native integrations for enterprise programs.
• Guarantee enterprise-grade governance and observability with centralized asset monitoring, built-in monitoring, and automatic compliance reporting throughout any setting.

With NVIDIA AI Enterprise embedded into DataRobot, you may:

• Use performance-optimized AI mannequin containers and enterprise-grade improvement software program.
• Simplify deployment setup with NVIDIA NIM and NeMo microservices, that work out of the field.
• Pull deployed NIM fashions into the DataRobot playground and begin constructing with out configuration complications.
• Speed up collaboration throughout AI and DevOps groups to deploy brokers rapidly.
• Monitor and routinely enhance all deployed agentic AI apps throughout environments.

10 steps to take brokers from prototype to manufacturing

Comply with this step-by-step course of for utilizing DataRobot and NVIDIA AI Enterprise to construct, deploy, and scale brokers rapidly and effectively.

Step 1: Browse NVIDIA NIM gallery and register in DataRobot 

Entry a full library of NVIDIA NIM straight inside the DataRobot Registry. These pre-tuned, pre-configured parts are optimized for NVIDIA GPUs, supplying you with a high-performance basis with out guide setup.

When imported, DataRobot routinely applies versioning and tagging, so you may skip setup steps and get straight to constructing.

To get began:

1. Open the NVIDIA NIM gallery inside DataRobot’s registry.
2. Choose and import the mannequin into your registry.
3. Let DataRobot deal with the setup. It’ll suggest one of the best {hardware} configuration, permitting you to concentrate on testing and optimizing as a substitute of troubleshooting infrastructure.

Step 2: Choose a DataRobot app template

Begin compiling and configuring your agentic AI app with pre-built, customizable templates that remove setup work and allow you to go straight into prototyping, testing, and validating.

The DataRobot app library supplies frameworks designed for real-world deployment, serving to you rise up and operating rapidly. 

1. Choose a template that finest matches your use case.
2. Open a codespace, which comes pre-configured with setup directions.
3. Customise your app to run on NVIDIA NIM and fine-tune it on your wants

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

Along with your app template in place and {hardware} chosen, it’s time to herald the generative AI element and begin constructing your vector database (VDB) within the DataRobot Workbench.

1. Open your NVIDIA NIM within the DataRobot Workbench. A use case will likely be created routinely.
2. Join your knowledge and navigate to the Vector Databases tab.
3. Choose knowledge sources and select from a number of embedding fashions. DataRobot will routinely suggest one and supply options to check.

   You may also import embedding and reranking fashions from NVIDIA in DataRobot Registry and make them out there with the VDB creation interface.

4. Construct one or a number of VDBs to check efficiency earlier than integrating them into your RAG workflow within the subsequent step. 

Step 4: Check and consider NVIDIA NIM LLM configurations within the LLM Playground

In DataRobot’s LLM Playground, you may rapidly construct, evaluate, and optimize totally different RAG workflows and LLM configurations with out tedious guide switching.

Right here’s easy methods to check and refine your setup:

1. Create a Playground inside your current use case.
2. Choose LLMs, prompting methods, and VDBs to incorporate in your check.
3. Configure as much as three workflows at a time and run queries to check efficiency.
4. Analyze outcomes and refine your configuration to optimize response accuracy and effectivity.

Step 5: Add predictive parts to your brokers

(In case your app makes use of solely generative AI, you may transfer on to packaging with guardrails and closing testing.)

For brokers that incorporate forecasting or predictive duties, DataRobot streamlines the method with its built-in predictive AI capabilities.

DataRobot will routinely:

• Analyze the info, detect characteristic sorts, and preprocess it.
• Practice and consider a number of fashions, rating them with the best-performing one on the high.

Then you may:

• Analyze key drivers behind the prediction.
• Evaluate totally different fashions to fine-tune accuracy.
• Combine the chosen mannequin straight into your agent.

Step 6: Add the fitting instruments to your app 

Increase your app’s capabilities by integrating further instruments and brokers, such because the NVIDIA AI Blueprint for video search and summarization (VSS), to course of video feeds and remodel them into structured datasets.

Right here’s easy methods to improve your app:

• Create further instruments or brokers utilizing frameworks like LangChain, NVIDIA AgentIQ, NeMo microservices, NVIDIA Blueprints, or choices from the DataRobot library.
• Increase your knowledge sources by integrating hyperscaler-grade instruments that work throughout cloud, self-managed, and bare-metal environments.
• Deploy and check your app to make sure seamless integration together with your generative and predictive AI parts.

Step 7: Add monitoring and security guardrails 

Guardrails are your first line of protection in opposition to dangerous outputs, safety dangers, and compliance points. They assist guarantee AI-generated responses are correct, safe, and aligned with consumer intent. 

Right here’s easy methods to add guardrails to your app:

1. Open your mannequin within the Mannequin Workshop.
2. Click on “Configure” and navigate to the Guardrails part.
3. Choose and apply built-in protections comparable to NVIDIA NeMo Guardrails, together with:

   Keep on Matter
   Content material Security
   Jailbreak

4. Customise thresholds or add further guardrails to align together with your app’s particular necessities.

Step 8: Design and check your app’s UX

A well-designed UX makes your AI app intuitive, priceless, and simple to make use of. With DataRobot, you may stage a whole model of your app and check it with finish customers earlier than deployment.

Right here’s easy methods to check and refine your UX:

• Stage your app in DataRobot for testing.
• Share it through hyperlink or embed it in a real-world setting to collect consumer suggestions.
• Acquire full visibility into how the app works, together with chain of thought reasoning for transparency.
• Incorporate consumer suggestions early to refine the expertise and scale back pricey rework.

Step 9: Deploy your brokers with one-click

With one-click deployment, you may immediately launch NVIDIA NIMs from the mannequin registry with out guide setup, tuning, or infrastructure administration. 

Your app, guardrails, and monitoring are deployed collectively, making certain full traceability and governance.

Right here’s easy methods to deploy:

1. Choose the NVIDIA NIM mannequin you need to use.
2. Select your GPU configuration and set any vital runtime choices, all from a single display.
3. Deploy with one click on. DataRobot routinely packages and registers your mannequin with all vital parts.

Step 10: Monitor and govern your deployment in DataRobot

After deployment, your agent requires steady monitoring to make sure long-term stability, accuracy, and efficiency. NIM deployments use DataRobot’s observability framework to floor key metrics on well being and utilization.

The DataRobot Console supplies a centralized view to:

• Observe all AI purposes in a single dashboard.
• Determine potential points early earlier than they influence efficiency.
• Drill down into particular person prompts and deployments for deeper insights.

Break the iteration cycle

Complicated AI initiatives stall when groups spend an excessive amount of time swapping parts, tuning mixtures, and re-running exams to maintain up with evolving necessities. With out clear visibility or structured workflows, groups can simply lose observe of what’s working and waste time redoing the identical steps.

Greatest practices to scale back friction and keep momentum:

• Check and evaluate as you go. Experiment with totally different configurations early to keep away from pointless rework. DataRobot’s LLM Playground makes this quick and easy.
• Use structured workflows. Keep organized as you check variations in parts and configurations.
• Leverage audit logs and governance instruments. Preserve full visibility into modifications, streamline collaboration, and scale back duplication. DataRobot may also generate compliance documentation as a part of the method.
• Swap parts seamlessly. Use a modular platform that allows you to plug and play with out disrupting your app.

By adopting these practices, your staff can transfer sooner, keep aligned, and ship production-ready brokers with out getting caught in infinite iteration.

Make your brokers enterprise-ready

Agentic AI solely creates influence when it runs reliably in manufacturing and scales with out breaking belief.

DataRobot and NVIDIA AI Enterprise brings collectively pace, governance, and suppleness so you may construct, deploy, and handle brokers with confidence.

Whether or not you’re launching your first AI agent or scaling a portfolio of enterprise-grade options, this platform provides you the construction and reliability to show innovation into actual enterprise outcomes.

Able to construct? Ebook a demo with a DataRobot professional and see how briskly you may go from prototype to manufacturing.