What Breaks When AI Agents Run Unattended for Weeks

When an AI ops manager you reach by one text message decomposes the work, dispatches other agents, runs QA loops, and replies with the result, the real question is what silently breaks after weeks with nobody watching.

Hermes EA sits on that text channel. It takes the request, splits it into tasks, hands them to coding agents inside the OpenClaw runtime, checks the output with a judge loop, and sends the answer back. The whole chain runs on owned hardware: a Hetzner box called maxxx plus a local NVIDIA GB10 desktop for inference.

The stack stays up without constant babysitting. Hermes agents pull from a queue. They execute with the QA checks built in. Every step writes to a cross-server build log. Nerve, the observability dashboard, shows live agent activity across both machines.

After weeks of unattended runs the same patterns appear. Tasks can stall or produce results that require further checks. The build log records the work so failures stay traceable.

Human gates still matter at key points. Nerve surfaces cases that need review because it pulls from the same log the agents write.

The operational lesson is simple. Without the build log and the dashboard, a silent stall or a looping agent would stay hidden until something downstream broke. With them the failure stays visible and fixable before it compounds.

Most people add AI on top of old processes. The difference here is the operating layer underneath. The agents, the queue, the QA loop, and the observability tools run as one system on hardware you control.

What part of your current workflow would you actually trust to run for a week with zero checking?