Give AI agents a job, not the keys to your business

I believe AI agents are ready to do real work in small businesses. My advice to owners and managers is to put them to work, but not to confuse independence with usefulness. What matters is the harness around the agent: the instructions, boundaries, checks and people that turn a capability into a dependable contribution.

The picture I keep coming back to is a busy, happy restaurant kitchen. A new cook joins a brigade rather than being handed the building and told to make dinner happen. Recipes explain the method. Stations define the roles. The ticket rail carries the hand-offs and records what is waiting. At the pass, the chef tastes every plate before it reaches a guest.

That is how I would organise agents. Written procedures are the recipes. A clearly defined job is the station. A shared task record is the ticket rail. A named person provides approval at the pass. I see that structure not as a brake on useful work, but as what lets a newcomer get moving quickly without everyone else guessing what they are doing.

A harness does not make a strong horse weaker. It directs that strength towards useful work. I think the same principle should guide us here: do not celebrate how much freedom an agent has; ask whether its effort is going where the business needs it.

I would start by treating each agent like a new team member with one role. Give it a written brief explaining the task, the boundaries, the required result and when to stop and ask. Provide only the access that role needs. I would never give it passwords, payment access or the ability to message customers on its own.

My rule is simple: people decide; agents execute. Anything that cannot be undone, or that faces the outside world, must pass through a named person for approval. That includes money, customer communications, publishing and deleting. An agent can prepare a proposed action without being allowed to carry it out. I want the owner of that decision to be unmistakable.

I would make every hand-off explicit, too. For working purposes, assume the agent knows only what you give it. Supply the relevant background, the current file and the next step, rather than expecting it to pick up an unspoken understanding. Before it starts, ask it to state the rules back. That gives you a chance to catch a misunderstanding before it becomes work to repair.

I would never let an agent approve its own work, and I would never accept “done” on its word. Check the real result: the record, the screen, the saved file. Keep a simple log showing what was requested, what changed, what was checked and who approved it. Use a status that anyone on the team can understand, including when something is blocked.

For a first project, I would choose one repetitive process that is easy to check. Write down how it works today and what good looks like. Run it for a few weeks, then compare the results, checking time and corrections with the old method. If supervising it swallows the benefit, I would narrow the task or improve the brief before expanding it.

Finally, I would give someone responsibility for keeping the harness current. Procedures change; permissions and checks need attention. This is a way of working, not a one-off installation. DevMojos helps businesses put AI agents to work with the harness in place. My guiding belief is that the best kitchens are fast because they are organised.