AI Agent Governance Guide
A practical guide to governing agent access
A practical guide for IT and security leaders on how to control what AI agents can reach, who can use which tools, and what agents know, so pilots can move into production.
AI agent pilots stall in security review. Every MCP server, from a vendor or built in-house, comes with its own access model, and teams set permissions one connection at a time. Pilots often get running on a shared service account, so when an agent changes a record, the system logs the account instead of the person the agent acted for.
That broad access also costs more. Agents pull more data and make more calls than the task needs. You pay for it in tokens, in less reliable answers and in shared rate limits that run out.
This guide explains why the right place for agent governance is in the platform that already runs your integrations and automations.
- The three questions every CIO has to answer before agents reach production systems
- Why access broader than the task needs costs money and reliability, not just security
- Five requirements to set before any vendor conversation
- How API management, AI and MCP gateways, identity platforms, agent platforms and integration platforms compare, and where each one falls short
- Why task-scoped tools and governed knowledge beat raw system access on cost, accuracy and how fast security can approve them