AI Agent Governance: How to Manage AI Agents | Webinar
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AI Agents Are Everywhere. Who’s Managing Them?
Build visibility, ownership, and control—without slowing innovation.
AI agents are becoming part of everyday work across productivity tools, SaaS platforms, automation, and employee-built workflows. This webinar explores key governance questions: who should own AI agents, which decisions require approval, and what controls IT and security should establish before an agent accesses critical data or systems.
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Key Takeaways
- Recognize where AI agents enter the business—including embedded assistants, low-code tools, and SaaS applications.
- Clarify ownership, accountability, and decision rights: who approves an agent, who manages it, and who can say stop.
- Define acceptable AI use, data boundaries, and the level of risk your organization is willing to accept.
- Build an inventory, intake, and approval process so AI use cases are known and reviewed.
- Connect policy to practical guardrails, monitoring, and incident handling.
- Establish an ongoing review process to recertify or retire agents as business needs change.
A Practical Framework for AI Agent Governance
A practical AI agent governance framework brings people, process, and technology together across five areas: accountability and decision rights; use policy and risk appetite; inventory, intake, and approval; controls and monitoring; and review and assurance.
- Who is responsible for AI agents?
AI agent governance should define who makes decisions, who manages each agent, and who has authority to pause or stop it. Clear accountability establishes who approves an agent and who oversees its use. - What should AI agents be allowed to do?
An AI agent use policy should define permitted activities, the data agents may access, and the level of risk the organization will accept. These boundaries guide decisions before agents access critical data or systems. - How should AI agents be reviewed?
An AI agent inventory, intake, and approval process makes AI use cases visible, assigns risk tiers, and establishes a clear approval path. Each use case should be known and reviewed before approval.
- How to enforce AI agent guardrails?
AI agent controls turn governance policies into operational guardrails. Teams should put controls in place, test that they work, monitor agent activity, and prepare to handle incidents. - How to maintain AI agent governance?
AI agent governance requires ongoing review as business needs change. Teams should regularly review agents, recertify or retire them, train staff, and report to leaders to maintain oversight.
Who This Webinar Is For
- Executive and business leaders defining accountability for AI adoption.
- IT and AI leaders overseeing agents across tools, teams, and workflows.
- Security, risk, and compliance teams establishing oversight and data-use boundaries.
Give Your AI Agents Clear Ownership and Boundaries
Ready to turn the discussion into a practical approach for your organization? Talk with ProArch about your AI agent governance priorities—from visibility and ownership to approval, controls, and ongoing review.
