Table of Contents
- What does it mean to run an AI agent in the background?
- Why does leaving an agent on your laptop fail?
- How do you run AI agents in the background with Agent 37?
- Can cron, systemd, or Docker keep an agent running on your own server?
- Which hosting option fits your situation: managed, VPS, Docker, or Mac mini?
- What keeps an unattended background agent from doing damage?
- When should you schedule jobs instead of running the agent continuously?
- What criteria shaped the hosting comparison table?
- What are the first steps to launch a background agent on Agent 37?
- Which background agent questions come up most often?
- Can I run an AI agent 24/7 for free?
- How do I schedule an AI agent to run every day?
- Does running an AI agent in the background use a lot of API tokens?
- What happens if my background AI agent crashes?
- Can I run background agents for clients under my own brand?
Do not index
The short answer to how to run AI agents in background is to put the agent on an always-on host with a scheduler and a process supervisor, then review its work on your own time. We recommend Agent 37, a managed hosting platform for OpenClaw and Hermes agents starting at $3.99 per month, because it ships with a task board for assigning work, scheduling recurring jobs, and reviewing outputs, so the agent keeps working after you close your laptop. Agent 37 is not a model provider: you bring your own Anthropic or OpenAI API keys, and those keys go directly to the provider rather than through Agent 37. If you prefer to self-host, cron, systemd, or Docker on a VPS also work, with more upkeep on your side.
Key takeaways
- An always-on host is non-negotiable: a background agent dies the moment a laptop sleeps, so it needs a server, a VPS, or managed hosting that never powers down.
- Agent 37 starts at $3.99 per month and includes a task board for scheduling recurring jobs, a browser terminal, a visual file browser, and a live Linux desktop via noVNC.
- Self-hosting works with three Linux tools: cron for time-based triggers, systemd for restart-on-crash supervision, and Docker for portable packaging.
- Isolation is the core safety control: Agent 37 runs each agent in its own container, and API keys go straight to Anthropic or OpenAI, which the company never touches.
- Most reliable setups combine one always-on listener (Telegram, Slack, or WhatsApp) with several scheduled tasks, instead of one giant polling loop.
What does it mean to run an AI agent in the background?
Running an AI agent in the background means the agent process stays alive on an always-on machine and does work without you watching it. Work is triggered by a schedule, an incoming message, or a queued task. The agent then reports results to a channel such as Slack, Telegram, or a task board you check later.
The key word is unattended. A chat session in ChatGPT or Claude.ai ends when you close the tab. A background agent keeps its runtime loaded on a server and waits for triggers. It is closer to a cron job that can reason than to a chatbot.
Three triggers cover almost every real use case. A time trigger runs the agent at 7am to summarize overnight email. An event trigger wakes it when a message arrives on Telegram or a row changes in a spreadsheet. A queue trigger lets you or a teammate drop a task on a board and walk away.
McKinsey describes this shift from passive answers to agents that execute multistep workflows in its analysis of why agents are the next frontier of generative AI. The infrastructure question in this post is how to keep that execution loop alive when nobody is at the keyboard.
Why does leaving an agent on your laptop fail?
A laptop sleeps, loses Wi-Fi, reboots for updates, and gets closed in a bag. Any of those events kills the agent process and silently stops your automations. Background agents need an always-on host with a process supervisor that restarts them on failure, plus persistent storage so memory and configuration survive a restart.
The failure is rarely dramatic. macOS and Windows both suspend background processes on sleep, and a forced update reboot wipes anything that was not registered as a service. You usually find out two days later when the digest never arrived.
A second problem is state. Agents like OpenClaw and Hermes keep memory files, configuration, and integration tokens on disk. If that disk belongs to a laptop that gets reformatted or replaced, the agent forgets everything it learned. An always-on host with persistent storage fixes both problems at once.
The third problem is attention. Running the agent in a terminal window on your main machine tempts you to babysit it. The whole point of background execution is that you check a task board or a Slack channel on your schedule, not the agent's.
How do you run AI agents in the background with Agent 37?
With Agent 37, you deploy an OpenClaw or Hermes agent with one click, add your own Anthropic or OpenAI API key, and assign work from the built-in task board. Recurring jobs run on a schedule you set, outputs land in the board for review, and plans start at $3.99 per month.
Here is the sequence we walk new users through.
- Create an instance. Sign up, pick OpenClaw or Hermes, and click deploy. The full agent stack comes up in its own isolated container with no manual install.
- Add your model key. Paste an Anthropic or OpenAI API key through the browser terminal. The key is sent to the provider directly, and Agent 37 never stores or proxies it.
- Connect your channels. Through Composio you can link Gmail, Slack, WhatsApp, GitHub, and 1000+ other integrations so the agent can read and act. Our walkthrough on setting messaging channels during OpenClaw setup covers the Telegram, Discord, and Slack options.
- Assign and schedule work. Open the task board, create a task, and set it to recur. A daily competitor pricing check, a weekly Reddit sweep, or an hourly inbox triage all live here.
- Review outputs. Completed tasks land back on the board. Use the file browser to inspect generated files, or open the live Linux desktop via noVNC when the agent needs a real browser session.
Why this beats a raw VPS for most people: runtime updates and security patches are applied automatically, so you are not SSHing in at midnight to fix a broken dependency. Agent 37 was #1 Product of the Day on Product Hunt, and its stated results include helping non-technical founders get agents running with guidance and community support rather than a sysadmin.
Can cron, systemd, or Docker keep an agent running on your own server?
On your own Linux server, three tools keep an agent alive: cron fires a command on a time schedule, systemd supervises a long-running service and restarts it on crash, and Docker packages the agent with its dependencies so it runs the same anywhere. Most self-hosters combine systemd or Docker for uptime with cron for scheduling.
Cron is the oldest tool and still the simplest. A single crontab line such as
0 7 * * * /opt/agent/run.sh daily-digest runs the agent every morning at 07:00. The crontab(5) manual page documents the five-field schedule syntax. Cron's limitation is that it does not care whether the previous run finished, so long jobs can overlap and double-spend tokens.systemd handles the always-on case. A unit file with
Restart=always and RestartSec=5 relaunches the agent within five seconds of a crash, and systemctl enable makes it start on boot. The systemd.service reference lists every restart policy. The cost is that you own the host: OS patches, disk space, and log rotation are your job.Docker adds portability and isolation. You build an image once and run it on any machine with the same behavior. If you go this route, our guide to running OpenClaw in Docker versus managed hosting walks through the compose file and the gotchas around persistent volumes. In practice, most self-hosters stack all three: Docker for packaging, systemd or Docker's own restart policy for uptime, and cron for schedules.
Which hosting option fits your situation: managed, VPS, Docker, or Mac mini?
A Mac mini works if you already own one and accept home-network risks like power cuts and ISP outages. A VPS gives you a datacenter connection but you still manage updates and patches yourself. Managed hosting removes both chores, which is why we recommend it for anyone who does not want to be the sysadmin.
Option | Best for | Pricing or format | Standout limit |
Agent 37 managed hosting | Non-technical users and agencies who want scheduling and review built in | From $3.99 per month, bring your own API keys | Hosts OpenClaw and Hermes runtimes, not arbitrary custom agent frameworks |
VPS with cron and systemd (DigitalOcean) | Developers comfortable with Linux who want full root control | Flat monthly fee per server; DigitalOcean publishes a $4 per month basic Droplet | You patch, back up, and restart everything yourself |
Self-hosted Docker | Teams that need identical environments across machines | Free software; you pay for whatever host runs the container | Persistent volumes and image updates are manual |
Mac mini at home | People who already own the hardware and want no recurring hosting fee | One-time purchase; Apple lists the base Mac mini at $599 | Home power and ISP outages take the agent down with nobody to restart it |
RunPod | GPU-heavy workloads that run local model weights | Per-hour GPU pod billing | Expensive for agents that only call hosted APIs over the network |
Choose managed hosting when you want the agent live today and would rather not learn systemd. Choose a VPS when you already run servers and want root access on a known bill. DigitalOcean's $4 per month Droplet is the published entry tier; confirm current pricing on digitalocean.com before you plan around it.
The Mac mini route is popular because the hardware is cheap to run and lives in your home. The trade-off is that a power cut, a router reboot, or a macOS update takes the agent down. We broke down the real numbers in our OpenClaw Mac mini guide covering specs, setup, and ongoing costs. Apple's published base price is $599; confirm on apple.com.
RunPod deserves a mention because people often reach for it first. It bills per hour for GPU pods, which is the right model if you run local LLM weights. For an agent that mostly calls Anthropic or OpenAI over the API, you pay for a GPU that sits idle.
What keeps an unattended background agent from doing damage?
Yes, if you isolate the agent, scope its credentials, and review its outputs. Run it in its own container so a bad tool call cannot touch other workloads, give it API keys and integrations limited to the job, and route results through a review step before anything is sent or published on your behalf.
Isolation is the first control. NIST's Application Container Security Guide, SP 800-190, recommends running each application in its own container with minimal privileges so a compromise stays contained. Agent 37 follows this model: your data stays in an isolated container, and your API keys go directly to Anthropic or OpenAI, so the company never touches them.
Least privilege is the second. Connect only the integrations the task needs. An agent that drafts LinkedIn posts does not need write access to your Gmail. Composio handles per-integration authorization, so you can grant Slack read access without exposing GitHub.
Review is the third. Set spending limits on your provider dashboard so a runaway loop cannot drain your account, and keep outputs on the task board until a human approves anything outward-facing. The agent does the work in the background; you keep the final say.
When should you schedule jobs instead of running the agent continuously?
Schedule jobs when the work is predictable: a morning digest, a weekly report, an hourly inbox sweep. Run the agent continuously when it must react to events as they happen, such as replying to a Telegram message or watching a channel. Many setups do both: one always-on listener plus several scheduled tasks.
Cost is the practical reason to prefer schedules. A continuous loop that polls every 30 seconds makes a model call each time, even when nothing changed. A scheduled run at fixed times makes a known number of calls per day, so your API bill is predictable.
The decision rule we use: if a delay of a few minutes is acceptable, schedule it. If someone is waiting for a reply, run a listener. OpenClaw's messaging channels are built for the listener case, and the Agent 37 task board covers the scheduled case, so you rarely need to write a polling loop yourself.
Coding work is a common hybrid. A background agent can pick up a queued ticket, run tests, and open a pull request while you sleep. Our guide to Claude Code background agent setups compares the ways to run that pattern without keeping a terminal open.
What criteria shaped the hosting comparison table?
We compared each approach on four things: the published entry price or billing model from each vendor's own pricing page, what you must maintain yourself (patches, restarts, backups), how work gets scheduled and reviewed, and how the agent is isolated from other workloads. We did not run benchmarks; the table reflects documented scope, not lab tests.
What are the first steps to launch a background agent on Agent 37?
Create an account, choose OpenClaw or Hermes, and deploy with one click. Add your Anthropic or OpenAI API key, connect the channels you already use, and create your first recurring task on the board. Plans start at $3.99 per month with your own API keys, and guidance plus community support are available for non-technical users.
Ready to run your first background agent? Deploy an OpenClaw or Hermes agent on Agent 37 and give it its first recurring task today.
Which background agent questions come up most often?
Can I run an AI agent 24/7 for free?
Only on hardware you already own, and you still pay for electricity, internet, and model API usage. The cheapest reliable path is a low-cost always-on host; Agent 37 starts at $3.99 per month with a bring-your-own-API-key model, so you pay the provider directly for tokens.
How do I schedule an AI agent to run every day?
On a Linux server, add a crontab line with a five-field schedule such as 0 7 * * * to run at 7am daily. On Agent 37, create a task on the built-in task board and mark it recurring; no cron syntax is needed and outputs return to the board for review.
Does running an AI agent in the background use a lot of API tokens?
It depends on the trigger. A continuous polling loop makes a model call every cycle even when nothing changed. Scheduled runs make a fixed number of calls per day, so costs stay predictable. Set spending limits on your Anthropic or OpenAI dashboard as a safety net.
What happens if my background AI agent crashes?
Without a supervisor, it stays down until you notice. On a self-hosted server, a systemd unit with Restart=always relaunches it automatically. On Agent 37, the managed runtime handles restarts, updates, and security patches, so you are not responsible for bringing it back.
Can I run background agents for clients under my own brand?
Yes. Agent 37 offers white-label capability with APIs and preloaded templates so agencies and consultants can resell hosted OpenClaw or Hermes agents under their own brand, with each client's data kept in an isolated container.