After a few months running an AI report generator for a client, the writing was never the hard part

I built an AI report generator for a client who sends a weekly performance summary to their customers. Pull the numbers, write the narrative, format it, send. They thought the value was in the writing. So did I at first. That turned out to be the easy 10%. The hard 90% was everything around the model. Getting the data in a clean, trustworthy shape before the agent ever saw it. Handling the week where a data source was down and the report should say "we don't have this yet" instead of confidently making something up. Deciding what happens when a number looks wrong, because a report that's fluent and confidently incorrect is worse than no report. The model itself, once it had clean inputs and a fixed structure, was almost the least interesting piece. It wrote the paragraphs. Fine. But every serious failure we had was upstream of the writing. A stale number, a missing field, a metric that changed definition and nobody told the agent. The lesson I keep relearning is that a generation agent is mostly a data and validation problem wearing a language costume. If you spend all your time on the prompt and none on what feeds it, you ship something that reads beautifully and is occasionally, invisibly wrong. And invisibly wrong is the one failure mode a report can't have, because people make decisions off it. For anyone running generation agents in production, where do you put most of your guardrails? Upstream on the data, or downstream checking the output before it goes out?