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Run Any AI Agent 24/7 on Sokko: The Always-On Managed Runtime

Sokko Team9 min read

What a managed AI agent runtime 24/7 actually means

A managed AI agent runtime 24/7 is infrastructure that keeps an AI agent running as a long-lived process, restarts it automatically when it crashes, preserves its state between runs, and hands it a stable place to write files, hold secrets, and reach the network. The phrase bundles three separate promises. Managed means someone else owns the servers, the patching, and the plumbing. Runtime means there is an actual process alive and waiting, not a function that spins up per request and disappears. And 24/7 means it survives the boring failures that kill hobby setups: a laptop lid closing, a spot instance getting reclaimed, an out-of-memory kill at 3 a.m. when nobody is watching.

Most agent demos never test any of that. They run once, in a terminal, while you watch. Real agents that monitor a mailbox, chase a long research task, or babysit production need to stay up on their own. This post covers why session-based and laptop setups fall over, what "always-on" genuinely requires, and how Sokko keeps any agent running around the clock on cloud infrastructure we run for you, so you never touch a server.

If you want the conceptual groundwork first, the companion piece on always-on versus session-based AI agents explains the distinction in more depth. This article is the practical, product-led version: here is how you actually get one running.

Why laptops and session-based setups fail long-running agents

A session-based setup ties the agent's life to something temporary: a browser tab, an SSH session, a serverless invocation, or a script you started by hand. That is fine for a one-shot task. It falls apart the moment the agent needs to outlive the thing that launched it.

Here is what breaks, concretely:

  • Sleep and suspend. Close your MacBook lid and the process pauses. Timers stop, network sockets drop, and any agent waiting on a webhook or a schedule simply misses it.

  • Crashes with no restart. A model API times out, an unhandled exception propagates, the process exits with code 1, and nothing brings it back. You find out hours later when the work did not happen.

  • Lost state. A script that keeps its task queue, scratch notes, or conversation memory in RAM loses all of it on exit. The next run starts from zero because there was no durable workspace.

  • No isolation for secrets. API keys end up in a .env file next to your source, or exported in a shell history, instead of in a real secret store with access controls.

  • No observability. When something goes wrong you have no logs, no metrics, and no way to see what the agent was doing before it died.

  • Home network reality. Residential IPs get rate-limited and rotated, your ISP router reboots, and your power flickers. None of that is acceptable for something meant to run every hour.

Serverless functions solve the "always available" part but reintroduce the opposite problem: they are stateless by design and cap out at short execution windows (AWS Lambda tops out at 15 minutes, for example). A research agent that needs to think for two hours cannot live inside a 15-minute box. You need a process that stays resident, keeps its memory, and gets restarted by a supervisor when it falls over. That is the gap a managed runtime fills.

What "always-on" actually requires

Before reaching for any product, it helps to name the ingredients. An agent that genuinely runs around the clock needs all of the following, not just one or two:

  • A long-lived process that is supposed to stay resident, not a per-request handler that exits.

  • Automatic restart on crash, driven by health checks, with sane backoff so a crash loop does not hammer your model provider.

  • A persistent workspace, a real filesystem that survives restarts so the agent keeps its files, checkpoints, and memory.

  • Managed secrets, injected at runtime and scoped per instance, never baked into an image or a repo.

  • Scheduling, so recurring work (every 15 minutes, every night at 02:00) fires reliably even when no human is around.

  • Observability, meaning logs, metrics, and a way to open a shell and see what is happening right now.

  • Network controls and resource limits, so a runaway agent cannot exhaust the box or reach places it should not.

  • RBAC, so a team can share the platform without everyone holding root over everyone else's agents.

You can assemble all of this yourself with Kubernetes, and plenty of teams do. Deployments give you restart-on-crash, PersistentVolumes give you durable storage, CronJobs give you scheduling, Secrets give you key management, and NetworkPolicies give you isolation. The catch is that you now own a Kubernetes cluster. Someone has to keep the control plane patched, the nodes healthy, and the RBAC coherent. For a solo builder or a small team, that is a second full-time job. If you want a sense of what raw Kubernetes involves, the official Kubernetes documentation is a fair place to gauge the depth.

How Sokko provides a managed AI agent runtime 24/7

Sokko runs the servers and the always-on infrastructure underneath, so you get every ingredient above without operating anything yourself. You describe the agent you want; Sokko schedules it, keeps it alive, and gives you a place to watch it. The plumbing is real, but it is our job, not yours.

Concretely, the platform gives each agent:

  • Long-lived processes with automatic health checks, so a hung or crashed agent is restarted rather than left dead.

  • A persistent workspace the agent can read and write, backed by durable storage that survives restarts and redeploys.

  • Secrets management that keeps your API keys in secure storage, scoped to a single agent and out of your code.

  • RBAC and team management, so you can grant teammates access to specific instances without handing over the whole account.

  • Observability through streamed logs and metrics, plus a browser terminal for each instance at https://<slug>.sokko.ai where you can attach and poke at a running agent directly.

  • Resource and network limits that cap each agent's CPU, memory, and network use, so one agent can never starve or endanger the rest.

To skip boilerplate, Sokko ships instance templates that come preconfigured for common agent shapes:

  • OpenClaw, a gateway assistant: one persistent assistant you reach across WhatsApp, Telegram, Slack, and Discord, with 1,000+ community skills and tool-use.

  • Hermes, a self-improving agent with persistent memory and a skills library that sharpens itself after each task, built for long-running research and outreach that needs to keep going for hours or days.

  • Paperclip, a multi-agent orchestrator: an agent org chart of roles, goals, budgets, and heartbeats with a full audit log.

  • OpenSRE, a site-reliability agent that watches systems and responds.

You start from one of these four runtimes and configure it to your task, bringing your own model keys or using Sokko credits. Whichever you pick, the runtime guarantees (restart, persistence, secrets, observability) are the same. That is the whole point of a managed AI agent runtime 24/7: the reliability is a property of the platform, not something you re-implement per agent.

Getting started: from signup to a running agent

Here is the short path from nothing to an always-on agent.

  1. Create your agent. In the dashboard, pick a runtime, give it a name, and add your own model key. In a couple of clicks it is live: Sokko sets up the private space, storage, secrets, and networking for you, so you never touch infrastructure.

    Prefer the terminal? Developers can do the same from the sokko CLI. With Node 18+, install it, sign in with an API key from Settings → API Keys, and create the instance in one command:

npm i -g sokko
sokko login
sokko instances create --runtime hermes --name research-bot \
  --byok --provider openai --api-key sk-...

  1. Watch it run. Open the instance terminal in your browser at https://research-bot.sokko.ai, tail the logs, and confirm the agent is doing what you expect. Because the workspace is persistent, anything it writes is still there after a restart.

  2. Let it stay up. From here the agent runs on its own. If it crashes, the health check catches it and Sokko restarts it. If you scheduled recurring work, it fires whether or not you are online.

That is the entire loop. If your agent currently lives on your laptop, the migration is mostly moving your code into the workspace and moving your keys into secrets; the walkthrough in migrate a local AI agent to the cloud covers the mechanics. For a first-run tutorial with a minimal agent, see deploying your first agent.

When you do not need a managed AI agent runtime 24/7

Honesty matters here, because paying for always-on infrastructure you do not use is waste. You probably do not need a managed runtime if:

  • Your agent is a one-off script you run by hand a few times and then forget.

  • The task is short and synchronous, finishing in seconds while you watch, with no state to carry forward.

  • You are still prototyping and iterating faster than any deployment could keep up with, in which case your laptop is the right tool until the shape settles.

  • The work is truly stateless and bursty, in which case a plain serverless function or a scheduled GitHub Action may be cheaper and simpler than a resident process.

The signal that you have crossed into always-on territory is when you catch yourself saying "it needs to keep running even when I close my laptop," or "it should have picked that up an hour ago," or "why did it forget everything from the last run." Those are state, uptime, and restart problems, and they are exactly what a managed runtime exists to solve. Cost is a fair worry too; the tradeoffs between the cheapest self-managed options and a managed platform are laid out in the cheapest way to host an AI agent 24/7.

The rule of thumb: reach for a managed AI agent runtime 24/7 the moment your agent needs to outlive the session that started it. Below that line, keep it simple. Above it, stop hand-holding a process on your laptop and let a platform keep it alive. Sokko exists for the second case, so the interesting question stops being "is my agent still running" and becomes "what should my agent do next."