AI agents are useful because they can act: read a message, draft a reply, update a record, move a file. That same ability is why business owners reasonably ask, how do we stay in control?
The answer isn't a single setting. It's a handful of practical controls, designed in from the start. None of them are complicated, and together they make an agent's work visible, limited, and reversible.
1. Approval points
The simplest control is also the most important: decide which actions need a person's sign-off.
A useful way to think about it is in three tiers:
- Draft only. The agent prepares something — a reply, a summary, a filled-in form — and a person decides what happens to it.
- Act, then report. The agent takes a routine action (filing a document, tagging a request) and records it for a person to review later.
- Act freely. The agent handles a low-risk action without review.
Start new agents at the most cautious tier for everything. Move individual actions to a less cautious tier only after you've watched the agent handle them well. And keep anything that affects customers, money, or regulated information firmly in the first tier.
2. Least access
An agent should be able to reach only the systems and information its job requires — nothing more.
If an agent sorts incoming inquiries, it needs to read the inquiry inbox. It doesn't need your finance folder, your HR records, or permission to delete anything.
Write down what each agent can access, and review that list whenever its job changes. Where possible, give agents their own accounts or credentials rather than borrowing a person's, so their access can be limited and revoked independently.
3. Activity records
You can't supervise what you can't see. Every agent should keep a record of what it did: what it received, what it decided, and what action it took.
Those records serve three purposes:
- Checking: a person can review a sample of the agent's work.
- Investigating: if something goes wrong, you can see exactly what happened.
- Improving: patterns in the record show where the agent's instructions need adjusting.
The record doesn't need to be elaborate. It needs to exist, and someone needs to look at it.
4. A pause switch
Every agent should have a clear, simple way to stop it — and more than one person should know how.
If an agent starts behaving unexpectedly, the right first move is almost always to pause it, let the work fall back to your normal process, and investigate. That's only possible if pausing is easy and the fallback process still exists.
5. Escalation
Agents will meet situations they weren't designed for: an unusual request, an upset customer, a document they can't read. Decide in advance what happens then.
A good default is that when an agent is unsure, it stops and hands the item to a named person, with a short note explaining why. An agent that says "I'm not sure — passing this to you" is behaving well.
6. Regular review
Your business changes. So do the tools agents depend on. Set a regular time — monthly works for many small businesses — to look at each agent and ask:
- Is it still doing the job we need?
- What did people have to correct, and why?
- Has its access grown beyond what it needs?
- Should any action move to a more (or less) cautious tier?
Short, regular reviews catch drift before it becomes a problem.
A short checklist
Before any AI agent goes live, you should be able to answer yes to each of these:
- We've written down the agent's job, inputs, and outputs.
- We've decided which actions need approval, and started cautious.
- The agent can access only what its job requires, and we've documented it.
- The agent keeps a record of what it does.
- At least two people know how to pause it, and the manual process still works.
- We've decided who handles items the agent escalates.
- We've scheduled the first review.
Control is a design choice
Keeping people in control isn't about distrusting technology. It's about designing a system where people and AI each do what they're best at — and where the people responsible for the business can always see, check, and change what the AI is doing.
That's how we approach every project; you can read more in our approach, including how we handle access, data, and human oversight.
