Adoption of AI agents lags behind general LLM usage because users must justify significant setup overhead, which includes connecting accounts, defining workflows, and monitoring results, which often exceeds the time saved on smaller or infrequent tasks. Mainstream breakthrough will likely come not from people configuring agents themselves, but from embedding agent capabilities invisibly into existing tools and new products that deliver obvious, measurable value without users needing to understand the underlying automation.