What's one AI workflow you thought would be easy but turned out to be harder than expected?
A thread collecting real-world stories of AI workflows that looked simple but broke in small, annoying ways: research summaries, source monitoring, data cleaning, report generation. A recurring lesson is that the breaks live in the plumbing around the model, not in the model itself, and that separating fetching, judging, and delivering keeps the failure modes visible.