I built an AI content agent that would rather drop a topic than make things up

Most “AI writes your blog” setups fail in the same way: not with bad style, but with a confidently written falsehood. For most niches that is embarrassing. For an insurance website it is a real problem — a wrong sentence about a legal obligation or the scope of cover can mislead a reader.

This is how I designed an AI agent for gopoistenie.sk, a Slovak insurance website (motor liability, comprehensive cover, GAP, home and travel insurance). It prepares one article every week — and it is built so that a lack of facts is a reason to stop, not a reason to improvise.

The brief was not “generate articles”

The client needed regular content, because an insurance site lives on search: people look up what liability insurance covers or how GAP works, and if your site does not answer, someone else's will. But the real requirements were these:

• Choose topics from real data, not from whatever the model comes up with.
• Write nothing that is not backed by a verified source.
• Never repeat topics the site already covers.
• Publish nothing automatically — a person always has the final word.

One weekly run, step by step

The pipeline starts by itself every Monday at 6:00 with nobody involved:

1. Data analysis. It reads Google Search Console and Google Analytics: which pages people read, which queries bring them in, where content is missing.
2. Topic choice. Based on that data, the model picks one topic inside the site's areas — with a check against everything already published, so no duplicates.
3. Research from an allow-list of sources only. Laws on slov-lex.sk, government bodies, the regulator (the National Bank of Slovakia) and insurers' own websites. Nothing else.
4. The stop rule. If there are not enough confirmed facts for the topic, the article is not written and the topic is dropped. No “fill the gap with general knowledge”.
5. Writing. Claude writes the article in Slovak strictly from the collected facts.
6. Independent review. A separate AI step checks the draft for factual errors, outdated data, invented references and consistency with the sources. A draft with problems goes back for rework automatically.
7. Draft, not post. The checked article is saved to WordPress as a draft.

Three design decisions that matter more than the prompt

1. An allow-list beats “be accurate”

Telling a model to “only use reliable sources” does not work. Giving it a fixed list of sources and refusing to proceed without them does. The research step is the place where hallucinations are prevented — the writing step only has to stay inside the material it was given.

2. Dropping a topic is a valid outcome

Most pipelines treat “no article this week” as a failure, so they are built to always produce something. Here it is a normal, expected result. That single decision removes the pressure that makes generated text drift into made-up specifics.

3. The writer is not its own proofreader

The model that wrote the text is not the step that approves it. A separate review step compares the draft with the sources before it is saved at all — just as with people, an author should not be their own editor.

What stayed with a person

Publishing. The site owner reads the finished article in a dashboard and clicks Publish or Reject. Nothing else is required — but without that click, nothing reaches the site.

That is a deliberate decision, not a technical limitation. The site is responsible for what it publishes, and that responsibility should not be handed to an automaton. The agent saves the work of finding a topic, gathering material and writing; the decision stays with a human.

The result

The project is delivered and running every week. The client left a 5/5 review on the independent Slovak freelance portal Jaspravim.sk (in Slovak, translated): “Maksym built an AI agent that drafts blog articles for regular publishing. A professional approach, prompt communication, and the work was finished quickly with no delays.”

Takeaways if you are building something similar

• Put your effort into where facts come from, not into prompt wording.
• Make “not enough data → stop” an explicit, first-class path.
• Separate generation and verification into different steps.
• Keep a human approval step wherever being wrong has a cost.

The same pattern — data-driven topic, verified sources, separate check, human approval — works in any field where accuracy matters: finance, legal, health, technical documentation.

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Originally published as a case study on nexflow.sk. I build AI agents and automations for small businesses in Slovakia — see also AI agent vs chatbot vs automation: what your company actually needs.