DataRobot Agent Workforce Platform overview¶

The DataRobot Agent Workforce Platform provides a unified experience for building, operating, and governing enterprise AI solutions—predictive models, generative AI, and AI agents alike.

It is available on managed SaaS, virtual private cloud, or self-managed infrastructure—including on-prem and private GPU clouds, sovereign GPU clouds, air-gapped environments, and major public cloud platforms, co-engineered with NVIDIA and validated across infrastructure from Dell and Nebius.

Governance in DataRobot operates at three layers:

1. AI and agentic governance (approval workflows and versioning before an agent ships, plus real-time moderation that catches bias, hallucinations, prompt injection, and PII leakage in production).

2. IT governance (agent-specific identity and entitlements, with consistent lineage across agents, tools, and applications).

3. Infrastructure governance (gateways, fair-use policies, and cost controls that keep spend predictable at scale).

The same governance, monitoring, and control apply wherever it runs.

ワークベンチ¶

Building great AI solutions—predictive models, generative AI, and agentic workflows—requires a lot of experimentation. ワークベンチでは、エクスペリメントをすばやく実行して、複数のエクスペリメントを簡単に比較し、エクスペリメントのすべてのアセットを直感的なユースケースコンテナで整理できます。

レジストリ¶

Use Registry to create deployment-ready model and agent packages and generate compliance documentation for enterprise governance.

レジストリにより、すべてのAIアセットがドキュメント化され、バージョン管理下に置かれます。 With test results and metadata stored alongside each AI asset, you can deploy to production with confidence, regardless of whether the asset originated with DataRobot, was user-defined, was brought in from an external framework, or is an agent built with a third-party tool such as CrewAI, LangGraph, or LlamaIndex.

コンソール¶

Use Console to view the operating status of every deployed model and agent.

As your organization becomes more AI-driven, you'll have tens or even hundreds of task-specific models and agents running at once. Console provides a centralized hub for observing their performance, tracing agent decisions and tool calls back to their inputs, and configuring automated intervention and notification options to keep everything running smoothly and within policy.

ワンクリックでデプロイ¶

But where do deployments fit into the structure above? The deployment process is your model's or agent's seamless transition from Registry to Console. Once it's registered and tested in Registry, you'll have the option to deploy it with a single click.

Once you click to deploy, DataRobot's automation creates an API endpoint in your selected prediction or runtime environment and configures all the observability, monitoring, and governance controls for it.

これら4つのデプロイオプションがすべてサポートされています。

• DataRobotモデルをDataRobotサーバーレスまたは専用予測サーバーにデプロイ。
• DataRobotモデルを外部の予測サーバーにデプロイ
• カスタムモデルをDataRobotサーバーレスまたは専用予測サーバーにデプロイ。
• カスタムモデルを外部の予測サーバーにデプロイ

Agentic AI deployments follow the same registered, governed path into production.

Generative AI and agentic AI¶

DataRobot's generative AI capabilities build on DataRobot's predictive AI experience, and its agentic AI capabilities build on both. Bring your favorite libraries and frameworks (CrewAI, LangGraph, LlamaIndex, or a generic template of your own), choose your LLMs and vector databases, and integrate third-party tools—then embed or deploy the result wherever it drives value for your business, with governance built in at every layer.

Inside DataRobot there is very little distinction between predictive, generative, and agentic AI. The same underlying platform (Workbench, Registry, Console) supports building, operating, and governing all three. But you'll find that tutorials and examples are still organized along these lines, since the workflows differ:

• 予測AI

• Generative AI (GenAI) includes text and image generation using foundation models, typically as a single LLM call or RAG pipeline.

• エージェント型AI

Agentic AI in DataRobot

アプリケーション¶

DataRobot offers various approaches for building applications that allow you to share machine learning projects, generative AI workflows, and agents: custom applications, application templates, and no-code applications.

• カスタムアプリケーションは、カスタムコードを構築して実行するための簡単な方法です。
• カスタマイズ可能なアプリケーションテンプレートは、ユースケースをサポートするDataRobotのリソースをプログラムで生成することで、ユーザーを支援します。
• ノーコードアプリケーションでは、モデルを構築して評価することなく、DataRobotのコアサービスを利用できます。

データと会話するエージェントのアプリケーションテンプレートを試す

どちらのエクスペリエンスを選ぶべきか？¶

DataRobotで作業する場合、インターフェイスには2つの選択肢があります。

1. Log in to the platform via web browser and work with the graphical user interface (UI).
2. Access the platform programmatically with the REST API or Python client packages, or build agents with the DataRobot CLI in your own development environment (IDE, notebook, or coding agent of choice).

If you're leaning toward using DataRobot programmatically, explore the workflows in the UI first. DataRobotは、どちらのインターフェイスでも完全なアクセシビリティをお約束します。そのため、1つの選択肢に縛られることはありません。 以下を実行することが可能です。

• いつでもコードとUIを柔軟に切り替えることができます。
• 異なるオプションを使用している他のユーザーとシームレスにコラボレーションできます。
• When in code, use any development environment of your choice, and bring agents built elsewhere in for governance and deployment.

Try the UI¶

エクスペリメントベースの反復ワークフローを実現するワークベンチ、モデルの進化を追跡し、バージョン管理されたモデルを一元管理するためのレジストリ、デプロイされたモデルを監視・管理するコンソールで構成された、直感的なUIベースの製品です。 The UI provides a complete AI lifecycle platform, with broad interoperability and end-to-end capabilities for ML experimentation and production. It is also the gateway for creating GenAI experiments, Notebooks, and applications.

予測の基本ステップを試す

コード¶

A programmatic alternative for accessing DataRobot using the REST API or Python client packages, or the DataRobot CLI for agent development. You can also install DataRobot agentic skills in Cursor, Claude Code, Gemini CLI, and other coding agents to bring platform workflows into your IDE.

開発者クイックスタートを参照

ヘルプを得るには¶

以下のどこからでもヘルプを得ることができます。

次のステップ¶

さて、次は何をしましょうか？ DataRobotを習得する最善の方法は実践です。 Take an hour to complete these two suggested exercises.