#datarobot

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.

DataRobot now supports the Agentic Resource Discovery Specification, making DataRobot Agent Skills easier for AI clients, registries, and developers to find. Agents are only as useful as the capabilities they can reach. A coding agent can write code. A workflow agent can call tools. An enterprise agent can reason across systems. But all of that... The post DataRobot Agent Skills are now discoverable through Agentic Resource Discovery 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.