Most enterprises treat AI agents as tasks: fire one off, take the answer, discard everything it learned. That's why cost explodes and quality slips: every session starts cold, rebuilding context it already had and paying for it again. This talk makes the case for treating agents as learned infrastructure instead: persistent, collaborative, continually improving, and compounding in value the more your teams use them. When work and memory survive across sessions, models, and people, the economics flip: you lower spend, raise quality, and move faster at once, not as tradeoffs.
