Most "AI writer" products do the same thing: prompt → blob of text → paste. It works for a demo and falls apart in production, because the value isn't the prose; it's the domain expert's judgment, and a one-shot generation has nowhere to put it.
We build Larry, a tool that turns a lawyer's expertise into articles that get cited by Google, ChatGPT and Gemini. Here's the actual shape of our writing agent: three tools, an interview-first loop, and two patterns worth stealing for any "AI for experts" product.
The wrong shape: regenerate everything
The instinct is: user asks for a change → send the whole article back to the model → get a whole new article. This is bad because every regeneration drifts. The intro you liked changes. The one accurate statistic gets "improved" into a hallucination. You lose the expert's voice on every pass.
The fix is to treat the article like code: give the agent an editing tool that makes surgical changes to a document that already exists, not a firehose that rewrites it.
