by Dev Grover, GTM at OpenBox
A lot of teams start building AI agents before they think about AI governance. That probably makes sense. When you're experimenting, the focus is usually on getting the agent to work, not on documenting every decision it makes.
The challenge is that agents tend to become useful faster than expected. One day they're helping draft content. A few weeks later they're updating records, touching customer data, triggering workflows, or making recommendations that influence real decisions. That's usually when governance stops feeling like a future problem.
If I were putting together a simple checklist before deploying an AI agent, I'd start with a few basic questions. What data can it access? Who can approve its actions? Is there a record of what it did? Can changes be tracked over time? If something goes wrong, can someone reconstruct what happened and why? And if the agent produces a low-confidence result, does the workflow know how to handle it?
None of these controls are particularly complicated on their own. Permissions, approvals, audit logs, monitoring, version history, and failure handling have existed in software for a long time. What's changing is that AI agents are bringing those concerns into places where many teams haven't had to think about them before.
One thing I've noticed is that these questions rarely come up during the first prototype. They tend to appear once an agent becomes part of a real workflow and people start relying on its outputs. That's usually when teams begin thinking more seriously about visibility, approvals, accountability, and how decisions are being made over time.
Which of these do you already track in your AI workflows today?