Constructing production-grade agentic AI applications 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 price aims.
However growing agentic AI functions is just half the battle.
AI groups usually face challenges handing initiatives 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 fitting construction, agentic AI dangers getting caught in infinite iterations.
However when completed 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 technique 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 battle 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 tools. 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 sturdy growth and deployment surroundings, serving to groups transfer quicker from prototype to manufacturing whereas sustaining safety and enterprise readiness from day one.
Right here’s what this appears like.
The DataRobot agentic AI platform supplies an end-to-end platform to orchestrate and handle all the agentic AI lifecycle, enabling builders to construct, deploy, and govern AI functions in days as an alternative of months.
With DataRobot, customers can:
- Jumpstart growth with customizable agentic AI app templates that supply pre-built workflows tailor-made to widespread, high-impact enterprise issues.
- Streamline deployment of agentic AI apps on managed infrastructure utilizing built-in guardrails and native integrations with enterprise instruments and features.
- Guarantee enterprise-grade governance and observability with centralized asset monitoring, built-in monitoring, and automatic compliance reporting throughout any surroundings.
With NVIDIA AI Enterprise totally 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 functions shortly.
- Monitor and robotically 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 shortly 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 elements are optimized for NVIDIA GPUs, supplying you with a high-performance basis with out handbook setup.
When imported, DataRobot robotically applies versioning and tagging, so you’ll be able to 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 would advocate one of the best {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 eradicate 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 shortly.
- 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
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.
- Open your NVIDIA NIM within the DataRobot Workbench. A use case will probably be created robotically.
- Join your information and navigate to the Vector Databases tab.
- Choose information sources and select from a number of embedding fashions. DataRobot will robotically advocate one and supply alternate 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.
- Construct one or a number of VDBs to match efficiency earlier than integrating them into your RAG workflow within the subsequent step.
Step 4: Take a look at and consider NVIDIA NIM LLM configurations within the LLM Playground
In DataRobot’s LLM Playground, you’ll be able to shortly construct, evaluate, and optimize totally different RAG workflows and LLM configurations with out tedious handbook switching.
Right here’s take a look at and refine your setup:
- Create a Playground inside your present use case.
- Choose LLMs, prompting methods, and VDBs to incorporate in your take a look at.
- Configure as much as three workflows at a time and run queries to match efficiency.
- Analyze outcomes and refine your configuration to optimize response accuracy and effectivity.
Step 5: Add predictive parts to your agentic circulate
(In case your app makes use of solely generative AI, you’ll be able to 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 robotically:
- Analyze the info, detect function varieties, and preprocess it.
- Practice and consider a number of fashions, rating them with the best-performing one on the prime.
Then you’ll be able to:
- Analyze key drivers behind the prediction.
- Evaluate totally different fashions to fine-tune accuracy.
- Combine the chosen mannequin straight into your agentic AI app.
Step 6: Add the fitting instruments to your app
Develop 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 improve your app:
- Create extra instruments or brokers utilizing frameworks like LangChain, NVIDIA AgentIQ, NeMo microservices, NVIDIA Blueprints, or choices from the DataRobot library.
- Develop your information 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 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 akin to NVIDIA NeMo Guardrails, together with:
- 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’ll be able to stage an entire model of your app and take a look at it with finish customers earlier than deployment.
Right here’s 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 surroundings to collect person suggestions.
- Achieve 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 pricey rework.
Step 9: Deploy your agentic AI app with one-click
With one-click deployment, you’ll be able to 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 deploy:
- Choose the NVIDIA NIM mannequin you wish to use.
- Select your GPU configuration and set any obligatory runtime choices—all from a single display.
- Deploy with one click on. DataRobot robotically packages and registers your mannequin with all obligatory 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 functions in a single dashboard.
- Establish 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 infinite iteration
Advanced AI initiatives usually stall resulting from repetitive handbook work — swapping elements, tuning combos, and re-running assessments to fulfill 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.
Finest practices to scale back friction and preserve momentum:
- Take a look at 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 take a look at variations in elements and configurations.
- Leverage audit logs and governance instruments. Keep full visibility into modifications, streamline collaboration, and scale back duplication. DataRobot can even generate compliance documentation as a part of the method.
- Swap elements seamlessly. Use a modular platform that permits you to plug and play with out disrupting your app.
By following these practices, you and your crew can transfer quicker, keep aligned, and keep away from the iteration lure that slows down actual progress.
Develop and ship agentic AI that works
Agentic AI has large potential, however its influence depends upon delivering it effectively and making certain belief in manufacturing.
With DataRobot and NVIDIA AI Enterprise, groups achieve:
- 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 provides you the velocity, construction, and reliability to show innovation into actual enterprise outcomes.
Able to construct? Book a demo with a DataRobot expert and see how briskly you’ll be able to go from prototype to manufacturing.
