Developer finds AI agents hit an attention bottleneck, not a capability one

A software developer found that AI agents dramatically accelerated his work, compressing days of effort into hours across coding, research, and product tasks. However, after an initial productivity surge, a new constraint emerged: the agents could execute tasks efficiently but could not independently determine what needed to be done next. Every session required the developer to manually restore context — identifying stale pull requests, unresolved bugs, and pending decisions — effectively making him the scheduler and memory layer for the entire system. The core bottleneck was not model capability but the absence of a reliable routing layer to prioritize and surface work autonomously. This experience led him to recognize that the human operator becomes the runtime when AI agents lack a mechanism to manage attention and task sequencing on their own.