Attention recovery
The real benefit of persistent AI delegation: not faster execution, but more responsibility safely leaving your active attention so you stop holding a project together in your head.
Attention recovery is a way to describe what persistent agents could actually give back. If an agent can keep the relationship between a goal, the work already done and the systems where changes appear, it removes some of the mental overhead of keeping a project alive. The gain is not simply that things happen faster. It is that you can stop thinking about them.
This is a different test from "how much did the agent do while I was away?". A system that does a lot but requires constant checking has not recovered any attention; it has moved the work into supervision.
How to judge it
Ask whether more responsibility can safely leave your active attention, and whether you can stop managing the AI. That depends on clear authority limits, legible context and cheap recovery when the agent is wrong. It is the measure that persistent delegation products such as OpenAI Dots should be held to.
Read more in OpenAI Dots is a test of whether AI can carry a goal, not just complete a task.
Related terms
Persistent delegation
Handing an AI agent a goal it keeps carrying over time — remembering context, noticing relevant events and acting across applications — rather than completing a single prompted task.
Supervision load
The human attention an AI system still consumes — context supply, review, approval, correction and exception handling — measured per dependable, completed unit of work.
OpenAI Dots
OpenAI's always-on assistant, described as continuing to work across applications between prompts. It is useful as a test case for persistent delegation: can AI carry a goal, not just complete a task?