First time here?¶
Note
Prefer to learn with video? Visit YouTube to view the onboarding series to help get started with DataRobot.
The DataRobot Agent Workforce Platform provides a unified experience for building, operating, and governing enterprise AI solutions—AI agents, generative AI solutions, and predictive models.
Key Highlights:
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End-to-End orchestration and lifecycle management: Build, operate, and govern AI agents seamlessly.
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Deployment flexibility: Available across cloud partners, sovereign GPU clouds, on-prem/private GPU clouds, and major public clouds.
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NVIDIA-powered performance: Optimized for Blackwell GPUs, integrated with NIM and NeMo, with blueprints for rapid deployment.
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Enterprise-grade governance: Ensures secure, compliant, and scalable AI deployments.
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Validated design: Delivers streamlined, production-ready AI.
Know what you're looking for?¶
To get started quickly in the DataRobot Agent Workforce Platform, take a look at the overview and then jump directly into the two suggested learning exercises. Afterwards, continue your learning with more sample assets and tutorials.
| Get started | Jump in |
|---|---|
| Get a quick platform overview of the fundamentals of the DataRobot Agent Workforce Platform. | Start building agentic or GenAI projects, applications, or predictive models. |
Learn about Agent Assist (dr assist), an interactive AI assistant that helps users design, code, and deploy AI agents through natural conversation. |
Get started with Agent Assist or install DataRobot-supported agentic skills (Claude Code, Cursor, Codex, Gemini CLI, and other coding agents). |
| Learn the fundamentals of building a GenAI workflow. | Work with vector databases and RAG workflows; see some GenAI use cases |
| Learn the basics of foundational apps, DataRobot's solution to the challenge of scaling AI from local experiments to production systems. | Choose an application type and start building—custom apps, application templates, or no-code apps. |
| Review types of predictive modeling projects and sample use cases. | Build and evaluate predictive experiments. |
| Suggested first steps—try a generative or predictive walkthrough to get started. | See the sections above. |