This week in numbers (real, from our system)
- 🤖 AI agents running: 19
- 📝 Content published: 77 (blog RU 19, EN 28, Altezza 30)
- ⚙️ Generated programmatically: 4
- 📥 Leads in the system: 351 (+4 in the last 7 days)
Short answer: In our content factory, AI agents draft articles, build SEO pages, support sales conversations and report on analytics, while people set strategy, review the output and make the final call. It works because each agent has a narrow job, clear inputs and a human checkpoint.
Building in public usually means shiny screenshots. Let's be real: the interesting part is the plumbing. This weekly diary covers how our AI agents run content automation at Arxitek, what holds up and what keeps breaking. No hype. Figures are published in a separate block, not in this text.
How do AI agents split the work in a content factory?
We don't run a giant agent that does everything. That fails fast. Each agent owns a narrow job: a researcher gathers topics and sources, a writer drafts articles, a page builder generates SEO pages from templates, an analyst watches the data, and a sales assistant handles routine conversations. They pass structured handoffs, not vibes. When something goes wrong, we know which link broke.
What does a typical week look like?
Early in the week we review what analytics flagged: pages that stall, queries we miss, topics competitors cover better. Midweek, the agents draft and I edit. Late in the week we publish, read the results and adjust prompts and rules. The diary entry is mostly that last step: what we changed and why.
What still breaks in our content automation
Plenty. Agents drift in tone. They repeat themselves across articles. They sound confident about facts they can't back up. We fixed much of that with stricter rules: never invent data, use only sources that exist, flag gaps instead of filling them. Automation isn't set-and-forget. It's maintenance, like any other system.
Where do people stay in the loop?
Wherever judgement matters: strategy, positioning, final review, client conversations. AI agents free the team from repetitive routine. They don't replace the team. If agent output goes public without a person reading it, that's our mistake, not the tool's.
Frequently Asked Questions
What does building in public mean for a small business?
Sharing the real process, including mistakes, as it happens. It builds trust and forces you to be honest about what actually works.
Can AI agents run content automation without supervision?
Not responsibly. They handle volume and routine well, but review and accountability stay with people.
Where should a small team start with AI agents?
Pick a repetitive task, give it clear rules and a review step, and expand only once it's stable.
Why are there no figures in this article?
We publish numbers in a separate block so they stay accurate and verifiable, rather than scattered through the prose.
Conclusion
Building in public keeps us honest. The agents do the heavy lifting, and we do the thinking, checking and fixing. If you want to automate content in your own business, get in touch. We'll talk through what fits and what doesn't.