From answering to acting
A chatbot that writes a poor answer wastes a minute. An agent that acts on a poor answer changes the business. So the question for a leadership team isn't how clever the model is. It's what the agent may touch, who says yes, and how anyone would know what it actually did.
Our view: an agent that can act must be held to account like a person who can act. That comes down to three controls.
1. Only the access that person's role allows
An agent working for someone should see only what that person may see, and do only the job it's given. It shouldn't have a back door the person doesn't have.
In a platform we're building for a design and procurement business, the agents' tools check the same permissions as the screens people use. Automated tests check those permission rules on every release, so a change that would widen what an agent can reach fails before it ships.
2. A person's yes, given by the person
Approval only means something if the agent can't give it to itself. On the platform Futuvara runs on, an agent can't approve its own action. A person's yes comes from the person, in a place the agent can't write.
3. Claims checked against what really happened
Agents can report work they haven't done. The fix isn't to trust the model more. It's to check what the agent says against what the record shows.
On our own platform, when an agent in a meeting room says it saved something, that claim is checked against what storage actually holds, and any mismatch is flagged for a person to close.
Where this goes wrong
- Treating the agent as a super-user. Broad access "to make it work" is the fastest way to lose control of it.
- Approval by default. If the agent can mark its own work as approved, nobody approved it.
- Logs mistaken for accountability. A log helps only if it records the right things and someone reads it. Be wary of any vendor who promises a record of "everything".
Questions to ask before an agent touches your systems
- Does each agent get only the access of the person it works for, and is that tested on every release?
- Which actions wait for a person's yes, and can the agent ever give that yes itself?
- How would we find out if an agent said it did something it didn't?
- What exactly is recorded, and who reviews it?
Futuvara builds, implements and runs AI platforms where AI agents take on real work in your business, under rules you set, and earn the right to do more.


