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First time here?

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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:

  • End-to-End orchestration and lifecycle management: Build, operate, and govern AI agents seamlessly.

  • Deployment flexibility: Available across cloud partners, sovereign GPU clouds, on-prem/private GPU clouds, and major public clouds.

  • NVIDIA-powered performance: Optimized for Blackwell GPUs, integrated with NIM and NeMo, with blueprints for rapid deployment.

  • Enterprise-grade governance: Ensures secure, compliant, and scalable AI deployments.

  • Validated design: Delivers streamlined, production-ready AI.

Know what you're looking for?

Take a look at the options below to get started quickly in the DataRobot Agent Workforce Platform. Ship your first agent, read a platform overview, or try some suggested learning exercises.

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.
On a trial? Ship your first agent by building in the platform or in your own IDE. Build in the browser with codespaces, or build locally with DataRobot agentic skills.
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.