AI in my own business: how I work with AI agents

Joshua Maurer

Web Developer & Freelancer

November 2, 2026

Anyone who advises companies on AI should use it themselves. Not for a demo, but day to day, with real data and real consequences.

I run HafenPixel with my own back office in which AI agents do the groundwork. This article covers what they take on, what I still decide myself – and the rules I have set for it.

What the AI takes on

The agents work for me, not for themselves. Specifically:

• Analysis: bringing together search data, visitor numbers and keyword data and saying what matters. What used to take half a day of spreadsheets is now an overview within minutes.
• Research: competitors, trade media, legal changes – with sources, so I can check.
• Drafts: texts, posts, page concepts and, when needed, emails. A draft is always a proposal for me, never a result.
• Checking my own visibility: regular tests of how AI assistants such as ChatGPT or Gemini talk about HafenPixel. This has become one of my most important channels for enquiries.
• Tasks and reminders: whatever follows from an analysis lands in the system as a task with a date – not in my head.

What I decide myself

The line is simple: anything that goes outside or costs money, I decide. The table shows how the work is split.

And there is one thing no table captures: how it sounds. Whether a sentence sounds like me, I notice when I read it. No AI can do that for me.

An everyday example: marketing

This shows most clearly in marketing, the area that is most likely to slip alongside client projects. The agents take on much of the groundwork – and I decide what of it goes outside.

• Checking visibility: the agents analyse search data and compare it with my plan. Whatever does not hold up is dropped instead of me continuing to optimise in the wrong place – rule 5 has an example.
• Keeping an eye on AI assistants: regular tests check what ChatGPT or Gemini answer when someone asks for a provider like me. Where the answers have gaps, that becomes a task for the website. Because around a third of my enquiries come this way, this is now as important as Google.
• Source of every enquiry: every enquiry is recorded with its source. That shows me which channels actually lead to projects – and which only bring clicks.
• Research for guest articles: the agents find suitable trade media, with sources. Which ones I contact and what I write there is my decision.
• Content on request: texts are only created when I order them – rule 4 below shows how that is set up.

My five rules

1. Nothing goes out without approval

The line runs between inside and outside. Internally the agents may work freely: analyse, research, write drafts, create tasks. Anything that goes outside – an email, a post, a change to this website – only goes out after my approval. That costs a few seconds per item and prevents exactly the mistakes you cannot take back.

2. No figure without a source

Language models sound confident even when they are guessing. So the rule is: every figure I use has to go back to a source – my own data, a study, a document.

3. Client data only when it is cleared

What a client entrusts to me does not end up in a text, a reference or an analysis without asking. Some projects I mention only anonymously, some not at all. The agents know these rules and stick to them – and I still check.

4. Every project has a budget and a clear criterion

AI costs real money per request. Every project in my back office therefore has a fixed monthly budget and a success criterion I measure it against. This is what it looks like for a real example:

5. The AI may disagree – and so may I

A good agent says when an assumption is wrong. When reviewing my own plan for more visibility, the analysis showed that two of my assumptions were wrong. In turn, I reject suggestions that look good on paper but do not fit me. Both are normal and intended.

What works well – and what does not

Surprisingly important: good foundations. The more clearly my own rules, positioning and wording are documented, the better the drafts get. Most of the work was not in the technology, but in writing down how I actually work.

What this has to do with the AI literacy obligation

Under the European AI Act, companies that use AI have to support the AI literacy of their staff. The five rules above are essentially exactly that: clear agreements on what AI is used for, where its limits are and who decides what. In a team of several people, that includes everyone knowing and practising these rules. What the obligation actually requires is covered in the article AI literacy under Article 4 AI Act.

If you want to set up something like this for your team, these are the two ways I offer:

• AI training: half a day with your team, on your real tasks, including rules for everyday use.
• AI consulting: when the question is where AI really pays off in your business – and the implementation afterwards.

How I built AI into my own product is described in the case study on LaizyNote and the AI assistant Daisy.