Built a report generator agent for a client and the model was the smallest part of it

Built what a client kept calling an "AI report generator." The pitch was: raw operational data goes in, a clean formatted report comes out on a schedule. What I learned is that "generator" was doing a lot of hiding for how little of it was actually generation. Maybe 80% of the effort went into the inputs. The client's data lived in three places with mismatched labels, missing fields, and the occasional duplicate. No amount of clever prompting fixes garbage inputs, it just produces a very fluent report built on bad numbers. Once I spent the time normalizing the data upstream, the actual report-writing prompt was almost trivial. The other thing that surprised me: they wanted the report to look identical every week. That's a templating job, not a creativity job. So I locked the structure with a fixed template and only let the model fill in the narrative sections and flag anomalies. Letting it "design" the report each run gave inconsistent layouts that made week-over-week comparison annoying. So my honest take is a report generator is mostly a data pipeline with a thin language layer on top. The impressive-sounding part is the least of the work. For those doing similar builds, where do you draw the line between deterministic templating and letting the model write? I keep pushing more toward templates over time.