A long-running job can only be handed safely to an AI agent if it is detached from the agent’s call, so it keeps running after the call returns or the chat ends, and the agent can later retrieve its status, logs and exit code. Most “give an agent shell access” tools stop at the first half of that sentence, which is why a training run or a long build started through a plain shell call so often dies with the session.

There is no single best way to run long jobs through an agent; the right choice depends on where the job should run. AI Commander ranks first for jobs on machines you already own, because a detached job with an ID, logs, cancellation and GPU reservation is a core feature rather than something you assemble. Desktop Commander ranks second as the best free, open-source choice. Modal and Daytona move the job into a per-second cloud sandbox, and tmux over SSH is the vendor-free DIY route.

Why the ordinary shell call fails for long jobs

An agent’s shell call is built for seconds: it has a timeout, it returns output, and the agent moves on. Three things go wrong with a multi-hour task. The call times out and the process is killed with it. The chat ends and the connection that owned the process disappears. Or the job survives but the agent has no handle to find it again, so it cannot tell whether it finished, failed or is still running. A usable setup therefore needs two properties: detachment, and a stable identifier the agent can query later. Every option in this list is judged on how well it delivers those two.

Your machine or someone else’s cloud

The most important split is where the job runs. AI Commander, Desktop Commander and tmux over SSH run the job on hardware you control, which matters when the job needs your data, a GPU you already paid for or services on a private network. Modal and Daytona run it in the vendor’s environment, which is safer for untrusted code and removes the hardware question, but means moving data and dependencies there and paying by the second. If the job reads a 200 GB dataset sitting on a box in your office, a cloud sandbox is probably the wrong answer however good its pricing looks.

The restart caveat nobody should skip

Surviving a dropped connection is not the same as surviving a reboot or an agent update. AI Commander’s own documentation is candid here: jobs continue through network loss and the chat ending, but on Linux surviving an agent restart requires systemd and a root agent, and on Windows stopping the process tree ends jobs, so it advises assuming an unattended auto-update does. That is the right level of honesty, and the practical advice follows from it: before an overnight run, deliberately restart the agent once and confirm the job behaves the way you expect.

Scope the permissions, not just the transport

Detached jobs make an agent’s mistakes last longer. Neither AI Commander nor Desktop Commander is a sandbox; commands run with real operating-system permissions. For jobs that touch untrusted input, a disposable Modal or Daytona environment limits the damage, and for jobs on your own machine a dedicated low-privilege user is a cheap precaution.