Constructing production-grade agentic AI applications isn’t nearly assembling parts. 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 growing agentic AI purposes is just half the battle.
AI groups typically face challenges handing tasks off to DevOps or MLOps groups to face up the expertise, integrating them into current instruments and workflows, and managing monitoring, governance, and complicated GPU infrastructure at scale.
With out the suitable construction, agentic AI dangers getting caught in countless iterations.
However when achieved proper, agentic AI turns into extra than simply one other software. It’s a transformative pressure 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 approach to develop, deploy, and handle agentic AI effectively.
A whole AI stack for agentic AI improvement
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 method to improvement, 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 improvement and deployment atmosphere, serving to groups transfer sooner from prototype to manufacturing whereas sustaining safety and enterprise readiness from day one.
Right here’s what this seems to be like.
The DataRobot agentic AI platform offers 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 a substitute of months.
With DataRobot, customers can:
- Jumpstart improvement with customizable agentic AI app templates that supply 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 features.
- Guarantee enterprise-grade governance and observability with centralized asset monitoring, built-in monitoring, and automatic compliance reporting throughout any atmosphere.
With NVIDIA AI Enterprise absolutely embedded into DataRobot, organizations can:
- Use performance-optimized AI mannequin containers and enterprise grade-grade improvement 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 shortly.
- Monitor and robotically enhance all deployed agentic AI apps throughout environments.
10 steps to take agentic AI from prototype to manufacturing
Observe 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 parts are optimized for NVIDIA GPUs, providing you with a high-performance basis with out guide setup.
When imported, DataRobot robotically 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’ll suggest the very 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 get rid of setup work and allow you to go straight into prototyping, testing, and validating.
The DataRobot app library offers 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 to 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 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 can be created robotically.
- Join your knowledge and navigate to the Vector Databases tab.
- Choose knowledge sources and select from a number of embedding fashions. DataRobot will robotically suggest one and supply alternate options to check.
You may as well import embedding and reranking fashions from NVIDIA in DataRobot Registry and make them obtainable 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 shortly construct, evaluate, and optimize completely different RAG workflows and LLM configurations with out tedious guide switching.
Right here’s the right way to check and refine your setup:
- Create a Playground inside your current use case.
- Choose LLMs, prompting methods, and VDBs to incorporate in your check.
- 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 components to your agentic movement
(In case your app makes use of solely generative AI, you possibly can transfer on to packaging with guardrails and remaining 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 information, detect characteristic varieties, and preprocess it.
- Prepare and consider a number of fashions, rating them with the best-performing one on the high.
Then you possibly can:
- Analyze key drivers behind the prediction.
- Evaluate completely different fashions to fine-tune accuracy.
- Combine the chosen mannequin straight into your agentic AI app.
Step 6: Add the suitable 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 the right way 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.
- Develop 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 unhealthy outputs, safety dangers, and compliance points. They assist guarantee AI-generated responses are correct, safe, and aligned with person intent.
Right here’s the right way 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 resembling NVIDIA NeMo Guardrails, together with:
- Customise thresholds or add extra 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, helpful, and straightforward to make use of. With DataRobot, you possibly can stage an entire model of your app and check it with finish customers earlier than deployment.
Right here’s the right way to check and refine your UX:
- Stage your app in DataRobot for testing.
- Share it through hyperlink or embed it in a real-world atmosphere to assemble 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 cut 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 guide setup, tuning, or infrastructure administration.
Your app, guardrails, and monitoring are deployed collectively, guaranteeing full traceability and governance.
Right here’s the right way to deploy:
- Choose the NVIDIA NIM mannequin you need to use.
- Select your GPU configuration and set any needed runtime choices—all from a single display screen.
- Deploy with one click on. DataRobot robotically packages and registers your mannequin with all needed parts.
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 offers a centralized view to:
- Observe all AI purposes 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 countless iteration
Complicated AI tasks typically stall as a result of repetitive guide work — swapping parts, tuning mixtures, and re-running exams to fulfill 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 check variations in parts and configurations.
- Leverage audit logs and governance instruments. Keep full visibility into adjustments, streamline collaboration, and cut back duplication. DataRobot may 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 following these practices, you and your crew can transfer sooner, keep aligned, and keep away from the iteration entice that slows down actual progress.
Develop and ship agentic AI that works
Agentic AI has huge potential, however its influence is determined by delivering it effectively and guaranteeing belief in manufacturing.
With DataRobot and NVIDIA AI Enterprise, groups acquire:
- Pre-built templates to speed up improvement
- 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 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 possibly can go from prototype to manufacturing.
