Fence in your AI agent before it can send emails or spend money

I believe an AI agent should earn access to your business, not receive it as a welcome gift. If you run a small or medium-sized company, the attraction is understandable: help with emails, records, routine code and purchasing. But my position is simple. Before an agent can act on your systems, give it a fenced yard. Only then consider handing it keys.

I would not treat this as an argument against AI. I see it as the difference between trying a useful tool and giving an untested process authority over customers, money and operations. A persuasive demonstration would not tell me enough about how an agent handles an ambiguous request or an unexpected situation. I would want boundaries that hold even when its judgement does not.

My first step would be a sandbox or test copy: somewhere the agent can practise without contacting real customers, changing live records or making purchases. I would check that separation rather than assume it. Can the test account still reach a live inbox? Are real payment credentials available? If the fence has a gate into production, I would want to know exactly who controls it.

Next, I would give the agent its own account, with only the permissions its current task requires. Never my administrator login. An agent drafting appointment reminders has no obvious reason to alter payroll settings. I would also keep passwords and other credentials out of its reach unless they are essential to the authorised task. Separate access gives me something specific to restrict or revoke.

Then I would write down two categories: what it may do alone, and what needs a person to approve. Drafting an email might be allowed; sending it might require approval. Preparing a record update might be acceptable; deleting records might not. I would make those rules enforceable through system permissions and approval steps wherever possible, rather than relying only on instructions to behave carefully.

I would set limits on spend, volume and time before starting live work. That means a purchasing ceiling, a cap on messages or record changes, and a defined operating window. The right limits depend on the business and task. My test would be practical: if the agent repeated the wrong action until it reached the limit, could we comfortably absorb and reverse the result?

I would keep a log of its actions and read it closely during the first weeks. I would want to see what it changed, what it sent, which approvals it requested and where it stopped. For me, logging is not a box to tick. It is how I would compare the job I thought I had delegated with the job actually being done.

There must also be a stop button, and somebody must know they are responsible for pressing it. I would test that stopping the agent actually prevents further action, including queued work. I would also plan how to revoke its access and undo changes where possible. An emergency procedure is not much comfort if nobody knows where it is.

Only after reviewing real but low-risk work would I widen permissions, one responsibility at a time. I would look for consistent behaviour within the rules, not merely impressive output. In my view, the possibility of an agent crossing a boundary is reason enough to establish limits before trust, without waiting for a frightening example. My advice is to start small, watch closely and keep control. DevMojos helps businesses adopt AI agents safely, with the fences in place first.