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Agents working while I sleep

AI-generated diagram of scheduled routines, agents, subagents and human review.
AI-generated image

How many agents are working for you while you sleep?

Scheduled routines are a big part of my agent setup.

I define recurring tasks in advance and schedule when they should run. When a routine is due, an agent can start within that agreed scope without waiting for another prompt from me.

Alongside those routines, I delegate individual tasks to agents that work asynchronously while I do something else.

Depending on the model and agent setup, an agent can also spawn subagents automatically to work on parts of a task in parallel.

Some of that work can happen while I’m asleep.

For each routine, the setup needs to answer a few questions:

  • When should it run, and how often?
  • What should the agent achieve, and what counts as done?
  • Which context does it need, and where can it find it?
  • What is it allowed to do, and when must it stop for my approval?
  • Can it delegate to subagents, and within which limits?
  • Where should it save the result, and when should it notify me?

Shared context gives the agents a starting point. Explicit permissions set the boundaries. If the next action needs a new decision from me, it waits. A scheduled run doesn’t grant additional authority.

Command guardrails connected through hooks add a technical check for certain shell commands. Reviewing the output remains part of my job.

When I come back, I want a result I can inspect, a clear status or a specific question that needs my input.

I treat autonomy as a set of specific permissions—and scheduled routines make those permissions useful over time.

How many agents are working for you while you sleep—and what are they actually allowed to do?

Also published on LinkedIn and XING.