Short answer
Practical AI agent governance
Practical AI agent governance defines who can invoke an agent, what data and tools it may access, and when it must obtain human approval. Strong implementations also record actions, make connections revocable, and apply spending limits before costs are incurred.
1. Who can invoke it?
Role-based invocation prevents every person and channel from having the same authority. The company decides who can request which kinds of work.
2. What can it access?
An AI coworker should reach only explicitly connected tools through company-controlled access. Connections should be visible and revocable.
3. When does it need approval?
Always, Ask, and Never rules can be set per tool or action. Spending ceilings add another checkpoint before a request creates an unexpected cost.
Evidence after the action
An audit trail should make it possible to inspect what was requested, what the system did, which approval was used, and what result was produced. Marek is being built around this model; its final production controls are still undergoing QA.
Questions
What teams ask before joining
- Is a policy document enough for AI governance?
- No. A policy defines expectations, while product controls enforce identity, access, approvals, limits, and records during the work.
- Does governance eliminate every AI risk?
- No. It narrows authority and improves oversight, but organizations still need testing, human judgment, security practices, and incident response.