DataRobot Agent Skills are now discoverable through Agentic Resource Discovery

Enterprises can govern model context protocol (MCP) connections at scale by treating them as part of the agentic AI control plane. Every MCP server, exposed tool, permission, and agent relationship needs ownership, scope, monitoring, and auditability before it supports autonomous work. MCP governance is the discipline of controlling how AI agents discover, select, invoke, and... The post How can enterprises govern MCP connections at scale? appeared first on DataRobot.

Teams are moving AI agents from prototype to workflow fast. One agent gets connected to a document store. Another starts calling internal tools. A third begins touching customer data.  Soon, agents are operating across systems before governance teams have a clear record of what they can access, who owns them, or what they’ve done. AI... The post Shadow agents: find and govern unsanctioned AI agents appeared first on DataRobot.

Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others. ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer. It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engi

Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others. ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer. It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engi

The National Endowment for the Arts used an artificial intelligence coding agent to develop a replacement in a week for its legacy grants system.

Antigravity CLI is the newest agentic coding CLI from Google, replacing the now-deprecated Gemini CLI. It inherits the asynchronous subagent model that makes Antigravity stand out from the field, syncs bidirectionally with Antigravity Desktop, and is optimized for speed on Gemini 3.5 Flash. DataRobot ships a full plugin for Antigravity CLI directly from the same... The post DataRobot for Developers — integrating with the Google Antigravity CLI appeared first on DataRobot.

Claude Code is a genuinely good agent builder. You describe what you want, it reasons through the problem, picks tools, and ships working code. For greenfield projects against well-documented libraries, the experience is close to magic. Where it gets harder is the same place every coding agent struggles: building on a specialized platform with its... The post The DataRobot platform as skills in Claude Code appeared first on DataRobot.

Cursor has changed how developers write code. The agent mode is good: you describe what you want, it reasons through the problem, picks the right tools, and ships working code. For greenfield projects and standard libraries, it works smoothly. Where it gets harder is when you’re building agents on a specialized platform with its own... The post Build with Cursor and deploy production-ready AI agents on DataRobot appeared first on DataRobot.