Seven months with AI executives

Since March 2026, sales, orders and logistics, finance, production and marketing at Ampron have been in the hands of AI agents. There are ten seats. Each has its own role, its own identity, its own tools and written limits. There are four people: me as the decision-maker, the chief engineer, and two people in production and the warehouse. All of it runs on one server, in the EU, which we host ourselves. The reasoning is done by Claude models.

Seven months is long enough for the first excitement to wear off and ordinary work to begin. Here is what that time taught me. Not how the system is built, but what I would do again and what I would do differently.

What stays with people

The most important document is a short list. Only a person moves money, signs legal documents, hires anyone, sets the lowest allowed price or decides on ownership. Everything else can go to an agent.

That list is shorter than I expected. But every item on it is a point where a mistake can no longer be undone. The money is gone, the contract is signed, the person is hired.

At first I thought it was enough to have that list in the agent’s instructions. It is not. A rule that lives only in the instructions is a suggestion. The model can reason its way to why this time is different. That really happened to us. Now every rule that matters lives in code, at the level of the tools. An agent with no payment tool cannot make a payment. It does not have to remember anything.

My own job has become narrower. Work reaches me through three doors. The first: “I finished this, please accept it.” The second: “I need a decision that only you can make.” The third: “I can’t go on, and here is why.” An agent cannot mark its own work as done. A person does that. When a customer asks for a quote, my part is one approval. One line arrives in the chat: “Send?” I answer.

Why everything is written down

The second lesson is about writing. An agent is not a program that runs all the time. It wakes, does its work, writes down what it did, and stops. Between two runs, nothing thinks. Next time, the agent knows only what has been written down.

That forced me to write down things that had lived in people’s heads until then. We have a company constitution with values and hard limits. We have a decision register with evidence. We have a register that says which system is right when the data does not agree. Every agent has a written charter: what its domain is, what it may do alone, how it speaks, and when it must ask a person.

An unwritten rule exists too. It is just that nobody shaped it on purpose. The charter is also my main management tool. When I change the text, the agent’s behaviour changes within fifteen minutes.

Memory lives in the company’s systems, not in the model. Every agent keeps a diary, but the diary only says where to look. The system says what is true. When an agent writes “sent”, “paid” or “deployed”, that is checked against the system where it actually happened before it is repeated. Replace the agent or change the model, and the memory stays.

To me, that is what a system that does not need heroes means. The company’s knowledge does not belong to one head, whether that head is a person’s or a machine’s.

What broke

We made nine mistakes worth naming. I have already described one: a rule that lived only in the instructions.

Scheduled jobs stopped for days and nobody noticed. Now we check the real outcome, not the timer.

An agent promised “I’ll get back to you” and lost it on restart. Now every promise gets a tracked card in the same run.

A change was saved but never reached the running system. Now nothing is done until it is checked on the running system.

An agent repeated an outdated claim from its own notes. Agents replied to each other in a shared chat room. The whole team had one shared quota. When it ran out, everyone went quiet at once. A test copy touched live data.

In July we rebuilt the whole platform, because about thirty hand-copied services had drifted apart over time. Now each agent is described in one file, and everything else is generated from it.

None of these was a special kind of AI mistake. They were ordinary system failures: a silent fault, a lost promise, stale information. Organisations made of people make the same mistakes. The difference is what happens afterwards. After every mistake we look for the root cause by asking “why” five times. The fix is written back as a rule the platform enforces, or as a check that runs before every update. A lesson that lives only in someone’s memory is not a lesson.

What I would tell someone else

To anyone building the same thing, I would say five things.

1. First write down what only people do: money, signatures, hiring, and anything that goes out without review.
2. Start with an agent that can only read. It cannot break anything, and it shows you cheaply how things fail.
3. Build the roads to a person before you give anyone write access.
4. Let the agent only draft at first. Sending on its own comes later, and only once it has earned trust.
5. Measure human minutes per decision, not the number of tasks automated.

Most of the time did not go into AI. It went into rules, protection against failure, and connecting the systems we already had.

The model is the easy part. The hard part is deciding what stays with a person, and writing it down so that nobody can reason their way around it.