To keep an AI agent running while your laptop is off, run it on a Linux VPS with a dedicated non-root account, persistent storage, protected API keys, and a supervisor that starts the process at boot and restarts it after failure. For a single Python, Node.js, or Go worker, systemd is usually the simplest reliable choice. Use Docker Compose when the agent also needs a database, queue, browser, dashboard, or other services.
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The VPS normally handles orchestration, tool calls, webhooks, state, and network connections. If the agent sends prompts to a hosted model API, it generally does not need a GPU. Running a model locally is a different workload that can require substantially more CPU, RAM, disk, and possibly a GPU.
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What a 24/7 AI-agent deployment actually contains
An always-on deployment is more than leaving a script running in a terminal. A useful production layout has separate layers, each with a specific job:
- VPS operating system: A supported Linux distribution receives security updates and provides networking, users, filesystems, and service management.
- Agent runtime: Python, Node.js, Go, or another pinned runtime executes the agent and its dependencies.
- Supervisor: systemd or Docker Compose starts the process after boot and recovers it after an unexpected exit.
- Persistent state: Memory files, queues, databases, uploaded documents, and generated artifacts live in explicit directories or Docker volumes rather than an ephemeral working directory.
- Secret boundary: API keys and other credentials are loaded from a protected environment file or secret store, not committed to source code.
- Network edge: A reverse proxy such as Caddy, nginx, or Traefik terminates TLS and routes approved public traffic to the private agent port.
- Operations: Logs, health checks, resource alerts, backups, and a tested rollback or credential-revocation procedure keep a failure from becoming data loss or an unchecked security incident.
This arrangement lets you update the agent without tying it to your personal computer, home network, or a browser tab. It also makes the failure modes visible: a crashed worker, an exhausted disk, an expired certificate, and an upstream API outage are different problems and need different responses.
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Choose the process model before you rent a server
The right supervisor depends on whether you are running one process or a small application made of cooperating services. The following comparison is a practical decision guide, not a claim that one option is best for every agent.
| Option | Best fit | Always-on and recovery behavior | Persistence and operations | Main trade-off |
|---|---|---|---|---|
| systemd | One Python, Node.js, or Go worker | Starts at boot, restarts failed processes, and waits for network targets when configured | Uses normal Linux paths and journald; backups and secret permissions are your responsibility | Less convenient when the application grows into several dependent services |
| Docker Compose | An agent plus a database, queue, browser, dashboard, or sidecar | Compose restart policies recover containers; configure health checks and daemon startup explicitly | Named volumes or host mounts are required; a container filesystem should not be assumed durable | More moving parts, image maintenance, and secret-handling decisions |
| Cron and a script | Periodic checks, scheduled reports, or batch jobs | Runs on a schedule, but does not provide a persistent worker, connection, or queue consumer | Simple files and logs, but overlapping jobs and missed runs need handling | Not a substitute for a chat listener, webhook endpoint, or continuously polling worker |
| Managed service | Business-critical workflows where support and handoff matter more than host-level control | Provider-specific; verify boot behavior, failure recovery, and whether idle services scale to zero | May include managed logs, secrets, and backups, but retention and export rules vary | Less control and a recurring bill that can include platform, storage, and egress charges |
Hassan Tahir’s VoxFor guide, last edited July 10, 2026, summarizes the single-worker choice well: “For a small long-running agent, systemd is usually the simplest reliable process manager.” Choose Compose when service boundaries, reproducible images, or a multi-container dependency graph provide a real benefit rather than adding containers by default.
Size the VPS for the agent you are actually running
There is no universal “AI-agent” specification. Sizing depends first on where inference happens, then on the agent’s state and tool workload.
Hosted-model agents
When prompts are sent to an external model API, the VPS mainly performs orchestration, tool calls, storage, serialization, and network I/O. A small worker can therefore run on modest hardware, although browser automation, document indexing, concurrent jobs, and large local caches increase memory and disk use.
Local-model agents
If the VPS itself loads the model, RAM and CPU requirements rise sharply. Model weights, context windows, concurrent requests, quantization strategy, and accelerator support all affect capacity. A local model may require a GPU; do not apply a lightweight API-backed-agent estimate to this workload.
Published examples, with their limits
| Reference | Published capacity | How to interpret it |
|---|---|---|
| Moltis documentation (2026) | At least 1 GB RAM and 10 GB disk for its example VPS deployment | A minimum listed for that application, not a general standard for every agent |
| SIS Argentina provider guide (2026) | 2 vCPU and 2 GB RAM for one agent | A provider-specific sizing example; workload, runtime, and concurrency can require more |
Start with enough headroom that the operating system, runtime, logs, and swap are not competing with the agent. Watch memory pressure and disk growth during real workloads, then resize instead of guessing. Keep VPS charges separate from model API usage, storage, backups, domains, and monitoring. Introductory prices and renewal rates change, so check the current recurring price for your region and plan.
Provision and harden the Linux VPS
The commands below illustrate a Debian or Ubuntu-style setup. Package names and firewall syntax differ on other distributions, so adapt them to the image you selected.
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- Select a region and plan. Prefer a region close to users, webhook sources, and the model API when latency matters. Confirm data-residency requirements, provider reliability, backup options, and whether the plan permits the network traffic your tools need.
- Log in with the provider’s administrative account once. Update the operating system and install only the packages you need. Create a separate account for the agent:
sudo adduser --system --group --home /opt/ai-agent agent
sudo install -d -o agent -g agent /opt/ai-agent/app
sudo install -d -o agent -g agent /opt/ai-agent/data
sudo install -d -o agent -g agent /opt/ai-agent/logs- Harden administration. Add your SSH public key to the administrative account, test a second session, and then disable password authentication if your recovery process is ready. Do not expose SSH to the entire internet when your provider firewall can restrict it to your administrative addresses. Keep a documented emergency console or recovery path.
- Restrict inbound ports. A typical webhook deployment needs SSH plus TCP 80 and 443 for the reverse proxy. The agent’s internal port should remain bound to localhost or a private network. On a Debian/Ubuntu host using UFW, a starting point is:
sudo ufw default deny incoming
sudo ufw default allow outgoing
sudo ufw allow OpenSSH
sudo ufw allow 80/tcp
sudo ufw allow 443/tcp
sudo ufw enable
sudo ufw status verbose- Install the runtime and system dependencies. Use the agent’s supported Python, Node.js, or Go version, and pin dependencies where reproducibility matters. Avoid installing development tools or daemons that the service does not require.
Keep the service account non-root. If the agent can execute commands, define an explicit allow-list and approval boundary; a process that can write to production systems or invoke a shell needs stronger isolation than a read-only notification worker.
Install the agent with reproducible paths
Place source code under a directory owned by the service account, and keep mutable state separate from releases. A simple layout is /opt/ai-agent/app for code, /opt/ai-agent/data for memory and queues, and /opt/ai-agent/releases for versioned deployments. Create a virtual environment for Python or an equivalent isolated dependency directory for another runtime.
Pin the versions that affect behavior: the language runtime, lockfile dependencies, browser binaries, database client, and agent revision. Record the commit or release currently running. This makes a rollback possible when an upstream library changes tool-calling, authentication, or serialization behavior.
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Keep API keys and state out of disposable locations
Protect secrets
Create an environment file readable only by root and the agent group, or use your platform’s secret store. For a systemd service, for example:
sudo install -d -m 750 -o root -g agent /etc/ai-agent
sudo install -m 640 -o root -g agent /dev/null /etc/ai-agent/agent.env
sudoedit /etc/ai-agent/agent.envPut only the required variables in that file, such as the model API key, database URL, webhook signing secret, and allowed-origin setting. Do not commit the file to Git, paste keys into issue reports, or print the process environment in diagnostics. Rotate a key immediately if it appears in source, shell history, a ticket, or a log.
Make state explicit
Persist conversation memory, job queues, checkpoints, downloaded files, and generated outputs in known directories. A container’s writable layer can disappear when the container is replaced; a VPS disk can disappear when the server is destroyed. Use host mounts or named volumes for Compose, and back up important state to a different machine or storage service.
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Separate transient caches from irreplaceable state. This lets you clear a cache when disk usage is high without deleting the agent’s memory or an in-flight job database. Document the format, location, retention period, and restore command for each state component.
Run one worker with systemd
systemd is a strong default for a single long-running process because it integrates boot ordering, restart behavior, resource limits, and Linux logs.
- Create a unit file at
/etc/systemd/system/ai-agent.service:
[Unit]
Description=AI agent worker
Wants=network-online.target
After=network-online.target
[Service]
Type=simple
User=agent
Group=agent
WorkingDirectory=/opt/ai-agent/app
EnvironmentFile=/etc/ai-agent/agent.env
ExecStart=/opt/ai-agent/app/.venv/bin/python -m agent
Restart=on-failure
RestartSec=5
NoNewPrivileges=true
PrivateTmp=true
[Install]
WantedBy=multi-user.targetReplace the ExecStart path and module with your agent’s actual entry point. If the service needs a database or mounted disk, add the appropriate dependency and verify that the mount is available before startup. Do not put an API key directly in the unit file.
- Load, enable, and start the unit:
sudo systemctl daemon-reload
sudo systemctl enable --now ai-agent.service
systemctl status ai-agent.service --no-pager- Inspect logs without exposing secrets:
sudo journalctl -u ai-agent.service -n 100 --no-pager
sudo journalctl -u ai-agent.service -fUse Restart=on-failure for crashes rather than an unconditional tight loop that can create a restart storm. Add rate limits, memory limits, or a dedicated log-rotation policy after observing the workload. A process that exits cleanly on an unrecoverable configuration error should not be allowed to churn indefinitely; fix the configuration and restart it deliberately.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsUse Docker Compose for a multi-service agent
Compose is useful when the agent depends on a database, queue, browser, dashboard, or separate worker. Keep the deployment declarative, pin image tags or digests, and make every durable directory a volume or host mount.
services:
agent:
image: your-registry/your-agent:1.4.2
restart: unless-stopped
env_file:
- /etc/ai-agent/agent.env
volumes:
- agent-data:/var/lib/agent
expose:
- '8080'
healthcheck:
test: ['CMD', 'agent-healthcheck']
interval: 30s
timeout: 5s
retries: 3
volumes:
agent-data:The example keeps port 8080 private to the Compose network; the reverse proxy can reach it without making it directly accessible from the internet. Add a database or queue as a separate service only when the agent needs one, and give each service its own credentials and volume. A restart policy does not guarantee that a dependency is ready, that a failed health check will repair application state, or that data survives deletion. Test those cases.
Enable the Docker daemon at boot, bring the stack up after a reboot, and inspect both application and dependency logs. Back up the contents of named volumes with a documented restore procedure; recreating a container is not the same as restoring its data.
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Expose webhooks and dashboards through a protected edge
If the agent receives webhooks, serves a chat UI, or exposes a dashboard, put a reverse proxy in front of it. The proxy should terminate TLS for your domain, forward only the paths you intend to publish, and support WebSocket upgrades when the application needs them. Keep the agent bound to localhost or an internal container network.
Obtain a certificate through the proxy’s supported ACME workflow, renew it automatically, and monitor expiry. Add authentication before exposing a dashboard. For webhooks, verify the provider’s signature, reject stale timestamps or replayed event IDs, and limit request bodies and rates. TLS encrypts the connection; it does not authenticate callers or make an untrusted agent safe to expose.
Do not publish administrative ports, database ports, metrics endpoints, or a raw development server. If a public endpoint is not required, omit the reverse proxy and keep the worker outbound-only.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply a security boundary around agent actions
- Least privilege: Run as the dedicated service account and give it write access only to the directories it needs. Avoid unrestricted root or host-wide shell access.
- Network minimization: Allow only required inbound ports, restrict administrative SSH, and use outbound firewall rules when the agent’s integrations have a known destination set.
- Secret hygiene: Keep credentials outside source code, use restrictive permissions, avoid full prompt and token logging, and rotate credentials after suspected exposure.
- Tool permissions: Separate read-only tools from mutating tools. Require approval for deleting files, sending messages, changing production records, or executing commands.
- Isolation: For risky tools, use a separate container, restricted user, disposable workspace, or a dedicated worker rather than granting the main process broad host access.
- Updates: Patch the operating system, runtime, libraries, container images, reverse proxy, and browser dependencies on a schedule. Keep a known-good release for rollback.
- Data handling: Define what customer data, prompts, tool output, and generated files are retained, who can read them, and when they are deleted.
An always-on process increases the time available for an attacker to exploit a leaked key or vulnerable dependency. Treat the agent as an application with an identity and an authority set, not as a harmless background script.
Add health checks, monitoring, and backups
Health and resource signals
Monitor more than process uptime. Useful signals include CPU utilization, memory pressure and swap, disk space and inode usage, queue depth, job age, model API latency and error rates, authentication failures, certificate expiry, and the number of restarts. A process can remain “active” while every upstream request is timing out or a queue is growing without being consumed.
Expose a lightweight health endpoint or command that checks the parts required for useful work. A liveness check can confirm that the process is running; a deeper readiness check can verify database connectivity, writable state, and a recent successful upstream call without sending sensitive prompts.
Control logs
Set retention and rotation limits. Redact authorization headers, API keys, full prompts, private customer data, and model responses that contain sensitive information. Include timestamps, request or job IDs, severity, and a concise failure reason so an operator can correlate an error across the proxy, worker, and queue.
Back up state off the VPS
Back up databases, memory stores, configuration references, and generated files to storage that will survive VPS deletion. Encrypt backups, restrict access, define retention, and test a restore. A snapshot on the same provider or disk is useful for rapid rollback but is not an independent disaster-recovery copy.
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Write down how to provision a replacement VPS, install the pinned runtime, restore state, inject fresh secrets, point DNS, and verify health. Include the command that stops the old worker so two instances do not process the same queue. Record how to revoke every API key and webhook secret if the host is compromised.
Perform a controlled reboot and failure test
Do not assume “restart on failure” works because the unit file exists. Test the complete path during a maintenance window:
- Record the current service status, release identifier, queue depth, and last successful job.
- Restart the supervisor and confirm that the agent returns without manual intervention.
- Terminate the worker process and verify that systemd or Compose starts a replacement and records the event.
- Reboot the VPS. Confirm that the network, mounted volumes, Docker daemon if used, reverse proxy, dependencies, and agent return in the intended order.
- Send a harmless test event through the real webhook or queue and verify that state is written and the response reaches the caller.
- Temporarily block or simulate an upstream API failure. Confirm that retries use backoff, errors are visible, and a queue does not grow without bound.
- Restore normal connectivity, check that processing resumes without duplicate side effects, and inspect logs for leaked credentials.
This is an operational check for your deployment, not a guarantee that every agent or provider behaves identically. Repeat it after changing the runtime, container image, storage layout, or networking.
Troubleshoot the failures that most often break 24/7 agents
| Symptom | Likely cause | First checks |
|---|---|---|
| Service is inactive after reboot | Unit was not enabled, the Docker daemon was unavailable, or a dependency mounted late | Check systemctl is-enabled ai-agent.service, boot logs, dependency ordering, and container status |
| Repeated restarts every few seconds | Bad environment variable, missing file permission, incompatible dependency, or an immediate upstream failure | Read the service log, run the exact command as the service user, and add a sane restart delay |
| Agent loses memory or queues after deployment | State was stored in a container layer, temporary directory, or unmounted path | Inspect the configured volume or host directory, ownership, and backup restore procedure |
| Webhook returns timeout or 502 | Wrong private port, reverse-proxy route, firewall rule, TLS issue, or a worker blocked on a long task | Test locally first, inspect proxy and worker logs, and separate acknowledgement from background processing |
| Jobs accumulate while the process appears healthy | Worker concurrency is too low, an upstream API is throttling, or a hidden authentication failure is retrying | Check queue depth, API latency and status codes, token validity, and retry/backoff behavior |
| Disk fills unexpectedly | Unbounded logs, browser caches, generated files, or database growth | Measure each directory, rotate or expire safe data, and increase capacity only after removing the cause |
Decide when a VPS is the right home
A VPS is a good fit when you need predictable always-on execution, control of the operating system, a fixed network location, and the ability to choose your own runtime and storage. It also makes you responsible for patching, firewall rules, backups, certificates, monitoring, and incident response.
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Choose a managed service when the workflow is business-critical and support, auditability, or a handoff to another operator outweigh host-level control. Confirm that the platform does not scale the worker to zero during idle periods, and verify its limits for background execution, persistent disks, outbound connections, secrets, and logs.
For a single API-backed worker, begin with systemd on a modest Linux VPS and resize from measurements. For a multi-service application, use Compose with explicit volumes and health checks. Use cron only for genuinely periodic work. In every case, keep model API costs separate from infrastructure costs and test recovery before calling the agent 24/7.
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