IBM just showed why 1 “perfect” prompt loses to 4 boring ones. The task was simple: match missing grocery items with courier notes, reject nonsense reasons like “meh” and produce a clean report. One large prompt kept failing on edge cases. Not because the model was too small. It was being asked to extract, judge, compare and write at the same time. So IBM split it into 4 steps: extraction, validation, comparison and generation. Same model. Same data. Less intelligence required at each step. Every failure becomes visible and fixable. This is what most teams misunderstand about agents. The advantage is not putting 20 AI personas in a group chat. It is turning one vague task into narrow, testable decisions. Bigger models improve the demo. Better workflows survive production.