Constructing production-grade agentic AI purposes isn’t nearly assembling elements. It takes deep experience to design workflows that align enterprise wants with technical complexity.
AI groups should consider numerous configurations, balancing LLMs, smaller fashions, embedding methods, and guardrails, whereas assembly strict high quality, latency and value goals.
However creating agentic AI purposes is simply half the battle.
AI groups typically face challenges handing tasks off to DevOps or MLOps groups to face up the expertise, integrating them into present instruments and workflows, and managing monitoring, governance, and sophisticated GPU infrastructure at scale.
With out the appropriate construction, agentic AI dangers getting caught in limitless iterations.
However when executed proper, agentic AI turns into extra than simply one other software. It’s a transformative power empowering groups to construct scalable, clever options that drive innovation, effectivity, and unprecedented enterprise worth.
To make that leap, AI groups want extra than simply AI instruments. They want a structured, scalable strategy to develop, deploy, and handle agentic AI effectively.
An entire AI stack for agentic AI growth
Agentic AI can remodel enterprise workflows, however most groups wrestle to maneuver from prototype to manufacturing. The problem isn’t simply constructing an agent — it’s scaling infrastructure reliably, delivering actual worth, and sustaining belief within the outputs as utilization grows.
To succeed, AI groups want greater than disconnected instruments. They want a easy, unified, end-to-end strategy to growth, deployment, and administration.
How DataRobot, accelerated by NVIDIA delivers agentic AI
Collectively, DataRobot and NVIDIA present a pre-optimized AI stack, superior orchestration instruments, and a strong growth and deployment setting, serving to groups transfer quicker from prototype to manufacturing whereas sustaining safety and enterprise readiness from day one.
Right here’s what this seems like.
The DataRobot agentic AI platform supplies an end-to-end platform to orchestrate and handle your complete agentic AI lifecycle, enabling builders to construct, deploy, and govern AI purposes in days as an alternative of months.
With DataRobot, customers can:
- Jumpstart growth with customizable agentic AI app templates that provide pre-built workflows tailor-made to frequent, high-impact enterprise issues.
- Streamline deployment of agentic AI apps on managed infrastructure utilizing built-in guardrails and native integrations with enterprise instruments and capabilities.
- Guarantee enterprise-grade governance and observability with centralized asset monitoring, built-in monitoring, and automatic compliance reporting throughout any setting.
With NVIDIA AI Enterprise absolutely embedded into DataRobot, organizations can:
- Use performance-optimized AI mannequin containers and enterprise grade-grade growth software program.
- Simplify deployment setup with NVIDIA NIM and NeMo microservices, that work out-of-the-box.
- Quickly pull deployed NIM fashions into the playground and leverage DataRobot to construct agentic AI apps with out messing with configuration.
- Collaborate throughout AI and DevOps groups to deploy agentic AI purposes rapidly.
- Monitor and routinely enhance all deployed agentic AI apps throughout environments.
10 steps to take agentic AI from prototype to manufacturing
Comply with this step-by-step course of for utilizing DataRobot and NVIDIA AI Enterprise to construct, function, and govern your agentic AI rapidly and effectively.
Step 1: Browse NVIDIA NIM gallery and register in DataRobot
Entry a full library of NVIDIA NIM instantly throughout the DataRobot Registry. These pre-tuned, pre-configured elements are optimized for NVIDIA GPUs, providing you with a high-performance basis with out handbook setup.
When imported, DataRobot routinely applies versioning and tagging, so you possibly can skip setup steps and get straight to constructing.
To get began:
- Open the NVIDIA NIM gallery inside DataRobot’s registry.
- Choose and import the mannequin into your registry.
- Let DataRobot deal with the setup. It should suggest the perfect {hardware} configuration, permitting you to deal with testing and optimizing as an alternative 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 stand up and operating rapidly.
- Choose a template that finest matches your use case.
- Open a codespace, which comes pre-configured with setup directions.
- Customise your app to run on NVIDIA NIM and fine-tune it in your wants
Step 3: Open your NVIDIA NIM into DataRobot Workbench to construct and optimize your VDB
Together with your app template in place and {hardware} chosen, it’s time to usher in the generative AI element and begin constructing your vector database (VDB) within the DataRobot Workbench.
- Open your NVIDIA NIM within the DataRobot Workbench. A use case shall be created routinely.
- Join your knowledge and navigate to the Vector Databases tab.
- Choose knowledge sources and select from a number of embedding fashions. DataRobot will routinely suggest one and supply alternate options to check.
You too can import embedding and reranking fashions from NVIDIA in DataRobot Registry and make them out there with the VDB creation interface.
- 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 possibly can rapidly construct, evaluate, and optimize completely different RAG workflows and LLM configurations with out tedious handbook switching.
Right here’s tips on how to take a look at and refine your setup:
- Create a Playground inside your present use case.
- Choose LLMsprompting methods, and VDBs to incorporate in your take a look at.
- Configure as much as three workflows at a time and run queries to check efficiency.
- Analyze outcomes and refine your configuration to optimize response accuracy and effectivity.
Step 5: Add predictive parts to your agentic stream
(In case your app makes use of solely generative AI, you possibly can transfer on to packaging with guardrails and last testing.)
For agentic AI apps 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 varieties, and preprocess it.
- Prepare and consider a number of fashions, rating them with the best-performing one on the prime.
Then you possibly can:
- Analyze key drivers behind the prediction.
- Evaluate completely different fashions to fine-tune accuracy.
- Combine the chosen mannequin instantly into your agentic AI app.
Step 6: Add the appropriate instruments to your app
Increase your app’s capabilities by integrating extra 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 tips on how to improve your app:
- Create extra 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 take a look at your app to make sure seamless integration together with your generative and predictive AI elements.
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 person intent.
Right here’s tips on how to add guardrails to your app:
- Open your mannequin within the Mannequin Workshop.
- Click on “Configure” and navigate to the Guardrails part.
- Choose and apply built-in protections reminiscent of NVIDIA NeMo Guardrails, together with:
Keep on Subject
Content material Security
Jailbreak - Customise thresholds or add extra guardrails to align together with your app’s particular necessities.
Step 8: Design and take a look at your app’s UX
A well-designed UX makes your AI app intuitive, precious, and simple to make use of. With DataRobot, you possibly can stage a whole model of your app and take a look at it with finish customers earlier than deployment.
Right here’s tips on how to take a look at and refine your UX:
- Stage your app in DataRobot for testing.
- Share it by way of hyperlink or embed it in a real-world setting to collect person suggestions.
- Acquire full visibility into how the app works, together with chain of thought reasoning for transparency.
- Incorporate person suggestions early to refine the expertise and scale back expensive rework.
Step 9: Deploy your agentic AI app with one-click
With one-click deployment, you possibly can immediately launch NVIDIA NIMs from the mannequin registry with out handbook setup, tuning, or infrastructure administration.
Your app, guardrails, and monitoring are deployed collectively, making certain full traceability and governance.
Right here’s tips on how to deploy:
- Choose the NVIDIA NIM mannequin you need to use.
- Select your GPU configuration and set any mandatory runtime choices—all from a single display.
- Deploy with one click on. DataRobot routinely packages and registers your mannequin with all mandatory elements.
Step 10: Monitor and govern your deployment in DataRobot
After deployment, your AI app 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.
Keep away from getting caught in limitless iteration
Complicated AI tasks typically stall on account of repetitive handbook work — swapping elements, tuning combos, and re-running checks to satisfy evolving necessities. With out clear visibility or structured workflows, groups can simply lose monitor of what’s working and waste time redoing the identical steps.
Finest practices to cut back friction and keep momentum:
- Check and evaluate as you go. Experiment with completely 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 take a look at variations in elements and configurations.
- Leverage audit logs and governance instruments. Preserve full visibility into adjustments, streamline collaboration, and scale back duplication. DataRobot also can generate compliance documentation as a part of the method.
- Swap elements seamlessly. Use a modular platform that allows you to plug and play with out disrupting your app.
By following these practices, you and your staff can transfer quicker, keep aligned, and keep away from the iteration entice that slows down actual progress.
Develop and ship agentic AI that works
Agentic AI has large potential, however its influence will depend on delivering it effectively and making certain belief in manufacturing.
With DataRobot and NVIDIA AI Enterprise, groups acquire:
- Pre-built templates to speed up growth
- Optimized NVIDIA NIM containers for high-performance execution
- Constructed-in guardrails and monitoring for security and management
- A versatile, ruled pipeline that adapts to enterprise wants
Whether or not you’re launching your first agentic AI app or scaling a portfolio of enterprise-grade options, this platform offers you the pace, 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 possibly can go from prototype to manufacturing.
