Enterprise adoption of the Model Context Protocol is accelerating, and MCP has become the primary way agents connect to enterprise tools and data. But MCP is only part of how agents act. Agents also run CLIs, execute Skills, and generate code that calls APIs directly. Governing MCP well matters. Governing everything else agents can do matters just as much.
Building MCP servers and writing Skills isn't particularly hard. The real challenges are deciding which actions agents are allowed to take, controlling who can take them, and proving it all later. These are architectural questions, and they need answers before agents scale across an organization.
In this workshop, we will:
1. Show how to control agent actions with policies that apply across MCP servers, CLIs, Skills, and agent-generated code — including allowlists, access control by users and groups, and human-in-the-loop approvals.
2. Explain why enterprises need managed registries for MCP servers and Skills, and how admin review and approval change the trust model.
3. Work through audit and compliance requirements: capturing complete logs of agent and tool activity, exporting to enterprise storage, and generating reports.
4. Demonstrate how to discover shadow AI — unmanaged agents, MCPs, and Skills already running in your organization — and how to block them or bring them under management.
5. Look at token usage and spend visibility by agent, user, and group.
You'll leave with a clear picture of the architectural decisions ahead of you, and a better sense of what your security team will require before signing off on scaling AI agents across your organization.
