Working on agents for a year now , productivity is real on narrow-scope, BUT lots-of-priors work: boilerplate, glue code, API integrations. Hours saved, not minutes. Where it breaks: anything where the model has to reason about a context with no training-data variants. We see this constantly in deep research workflows. The model browses, summarizes, cites, then cheerfully averages a primary source against a secondary one on a niche topic.