A proactive agent needs to know when the work is finished, or I get a subscription to suggestions I never requested. The Gemini Spark announcement in May 2026 presented continuous tasks and proactive assistance. I like work advancing while I do something else. I want equal clarity about changing direction and ending the activity.
In June 2026, I asked for permanent agents and distributed work to gain speed. Then I asked for an agent to follow the others and maintain the shared objective. Looking back at the May announcement, that sequence explains what I want: people working in parallel need to stay on the same request. Even when those people are software.
The problem appears when following progress. To understand where a task stopped, I do not want to read every conversation from the beginning. I need to know what already contributes to the deliverable and what remains missing. A list of executed commands leaves me reconstructing that history. Without that explanation, I am still responsible for reconstructing the work. That leaves me as the context manager.
Consider recurring research as an example. The subject loses priority, but the assistant keeps supplying material. Execution works; usefulness has expired. I want to update the objective and keep what remains useful without reconstructing the entire investigation. When there is no reason to continue, stopping becomes part of completing the work.
The same applies to the coordination I requested. Two executions can be addressing the same question. One can be waiting for the other. Whoever follows their progress needs to notice that relationship before opening more work. Adding an agent to repeat the research just produces more answers for someone to assemble later.
Interruption has a cost too. A result that unlocks a decision deserves my attention. An intermediate detail can wait for the summary. I prefer adjusting that behavior around what I need to decide, instead of receiving a notification whenever the system demonstrates signs of life. The computer already has a light for that.
My next step is to establish whether a request has a final deliverable or a reason for continuous monitoring. I will make the expected result and stopping condition explicit, alongside who follows progress. Spark interests me if it helps sustain that continuity. When I return, I want to decide the next step without becoming an archaeologist of conversations.