AI Agent Governance Is Not Governing People: Why the Rules Change When Agents Act on Your Behalf

AI agent governance builds on familiar enterprise controls such as identity, permissions, policies, and audit trails, but differs because agents act on behalf of people while accountability remains with the deploying operator or user. Traditional role-based access models can create risk when agents receive standing service credentials or inherit a user’s full access, since an agent’s authority should instead be limited to a specific user, task, resource, and moment in time. The passage argues that governance decisions must occur at runtime, before each tool call, rather than through retrospective access reviews or logs, particularly when agents interact with sensitive systems such as Workday, SAP, and Salesforce. It cites the Air Canada chatbot case as an example of organizations being held responsible for agent-generated actions or statements, and notes that incomplete security approval remains a barrier to broader deployment. Arcade.dev is presented as a runtime enforcement layer intended to connect existing enterprise entitlement systems to agent actions, allowing agents to operate as delegates of named users under narrowly scoped, temporary permissions.

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