Most of us carry a mental model for production infrastructure that took years to build and mostly still works: stateless containers behind a load balancer, autoscaling on CPU or queue depth, a CI/CD pipeline that ships a new image, health checks that restart what dies.
It’s a good model.
It’s also the wrong model for a meaningful chunk of what an AI agent needs once it stops being a demo and starts running unattended, handling real workloads, for real users, continuously.
The temptation is to treat an agent deployment like “a web app that happens to call an LLM.” That framing survives for about a week in production before it starts producing incidents that don’t map cleanly onto anything in the standard web application runbook.
This happens because a handful of assumptions baked into the web app model quietly stop being true for deployed AI agents, and the operational moves that used to be safe for web applications (restart it, roll it back, bump the version) start carrying different consequences.
Look at the two most widely deployed open source agent frameworks right now, OpenClaw and Hermes, and the gaps show up immediately once you see how they actually run in production
The Gateway Problem: Compute That Isn’t Triggered By Requests
Capacity planning for a web app is a solved problem. You provision for concurrent connections and p99 latency, you autoscale on CPU or queue depth, and decades of tooling (APM, load testing, canary rollouts) exist to support that model. The problem is that none of it assumes the workload can act on its own schedule.
Both OpenClaw and Hermes run as a persistent background gateway process, not a request-triggered service that spins up to handle a call and disappears. That alone changes the cost model from “pay for spikes” to “pay for something that’s always running.” But the sharper problem is that these agents are built to act without being asked. OpenClaw ships a heartbeat mechanism that periodically checks for pending work and acts on it unprompted. Hermes has built-in cron scheduling that delivers automated runs to whichever channel you’ve configured. Neither of these maps onto a request-rate-based autoscaling signal, because there’s no external request driving the trigger. The workload originates from inside the system, and is often directed toward other internal and external components, such as vector stores, databases, internal Markdown files, webhooks, and comms channels.
There’s also no equivalent yet to “provision for p99 latency” when the actual cost driver is reasoning depth. Two tasks that look identical from the outside (same channel, same user, same prompt length) can burn wildly different amounts of compute depending on how many tool calls and reasoning steps the agent (more specifically, the LLM powering the agent) decides it needs. Nobody has a clean formula for that the way they do for HTTP throughput.
Statelessness Stops Being a Virtue
The best practice for web services is to be stateless and disposable. Kill any instance that’s misbehaving, and traffic reroutes automatically with no loss in the user experience. Do the same thing to an agent, and you’ll degrade, then eventually kill, the quality of its output in production.
Hermes is the clearest illustration of this because the framework is built around a self-improving loop that creates and refines skills from experience, using tiered memory rather than the flat context window most LLM applications rely on . That’s the differentiator Nous Research built the whole runtime around. OpenClaw carries the same category of risk with its own persistent memory and installable skill system.
Now, if you treat an AI agent like the disposable, stateless container a web app runs in, one you can kill and restart on a whim, you’re deleting the exact thing that made it more useful today than it was yesterday.
When Your Attack Surface Can Take Actions
A typical web app’s attack surface is mostly about data exposure, injection, and auth bypass. Worst case, the blast radius is contained to what that app can read or write in its own datastore.
OpenClaw’s core action layer is built around direct bash execution as a shared tool across agent workflows, giving it the ability to manipulate files, install dependencies, and invoke APIs and cloud workflows directly. That’s a fundamentally different blast radius than a compromised or buggy web service.
Agent-security research has already documented what this looks like in practice. A 2026 paper on multi-agent safety, SafeClaw-R: Towards Safe and Secure Multi-Agent Personal Assistants, recounts an account originally shared by Meta’s Director of AI Alignment, where an autonomous personal assistant misinterpreted an instruction and started deleting an entire email archive. Because post-hoc guardrails could not intervene in real time, an emergency server shutdown was required to stop the action.
Secrets sprawl compounds this. Model API keys, per-channel bot tokens, and any MCP or tool-level credentials all need secure storage and rotation, and CVE tracking becomes a continuous job rather than a one-time hardening pass, because the framework underneath you is a fast-moving target.
The Version Bump That Changes How Your Agent Thinks
A dependency update on a web app is usually a bug fix or a security patch with a blast radius you can reason about at the code level. An update to an agent framework, on the other hand, can change reasoning behavior, prompt handling, or tool-calling logic, not just code paths. The same category of change, a version bump, now carries a fundamentally different kind of risk.
OpenClaw and Hermes move fast enough that this isn’t a theoretical concern. Hermes ships frequent, numbered releases, with recent versions adding new platform adapters and provider integrations. OpenClaw’s rebranding history alone tells you the shape of the project is still actively changing.
Self-hosters are stuck choosing between staying current and risking a behavioral regression in production, or pinning a version and risking a missed security fix, usually with no vetting layer sitting between “upstream cuts a release” and “it’s now running against your users.”
Multi-Channel Support Runs Through a Single Point of Failure
OpenClaw and Hermes both ship native multi-channel support that covers Slack, Discord, Telegram, WhatsApp, and a long list of others, built directly into the framework.
The architecture underneath is where the real contrast with a web app shows up. A web app has one interface to secure and monitor: HTTP. Both of these frameworks, by comparison, route every connected channel through a single background gateway process. That’s efficient until an outage or misconfiguration in that one process takes every channel down at once.
And even with native support, per-channel setup is still real, ongoing work. Networking requirements aren’t uniform either: some channel connections avoid a public endpoint entirely, while others are webhook-based and require one, so your externally exposed surface area shifts depending on which channels you’ve turned on, independent of your own architecture decisions.
Rollback Doesn’t Mean What You Think It Means
Rolling back a web app means redeploying the previous container image or code version in the repository. You lose nothing meaningful because there was no meaningful state to lose in the first place.
Rolling back an agent that has accumulated memory or refined skills over time can mean losing real, accrued value. This is most concretely illustrated by Hermes’ explicit skill-refinement loop, where the entire point of running it for weeks is that it gets measurably better at your specific workflows.
Debugging autonomous behavior after an incident also needs actual log and decision-trail visibility because the question after an incident usually isn’t “was it up.” In the case of AI agents, the real question is “why did it decide to do that.”
That is also the reason why the backup strategy for web apps doesn’t work for AI agents. For AI agents, the backup strategy has to account for state and memory as first-class artifacts, not an afterthought bolted onto code and static asset backups.
What Actually Changes With Managed Hosting
None of the issues mentioned earlier gets fixed by reaching for a bigger VPS because these gaps are architectural, not a resource constraint. So, throwing more compute or storage at the problem doesn’t touch the underlying mismatch between how web apps are supposed to run and how these agents actually behave.
This is where Cloudways Managed AI Agents is worth looking at because it addresses each of these gaps directly instead of leaving them as the operator’s problem.
Compute and integration are handled by deploying OpenClaw or Hermes from a dashboard you likely already use if you’re already on Cloudways, with the always-on gateway process and channel credentials (Slack, Discord, Telegram, WhatsApp) configured through that same interface instead of a separate vendor relationship and a pile of manifest files.
State and recovery are addressed by running each agent as an isolated instance with automated backups on every deployment. That gives the persistent memory and accumulated skills discussed above an actual restore point, which is the piece most self-hosted setups skip entirely until the first incident makes it painfully obvious.
Security is handled by validating runtime security patches before they roll out, rather than pulling whatever tag is newest off the framework’s release branch and finding out in production whether it broke something. Channel tokens and SSH/SFTP credentials live in one managed dashboard instead of scattered across .env files across however many boxes you’ve spun up.
Updates go through a real vetting layer. Instead of the binary choice of “stay current and risk a behavioral regression” or “pin a version and risk missing a security fix,” updates are validated before rollout, which directly closes the gap described earlier.
It’s also worth addressing why Cloudways offers both agents rather than picking one. OpenClaw brings a broad, fast-moving open source ecosystem with a large plugin and skill catalog and heartbeat-driven proactive behavior, while Hermes is built by an AI research lab around a first-party self-improving loop and native subagent delegation. Offering both side by side, on infrastructure that handles the operational floor for either one, is a more honest answer to “which agent framework should I run” than forcing a single opinionated choice.
And critically, none of this is a black box. SSH and SFTP access are retained on every plan, so you can still get in, read logs, and debug directly. Managed doesn’t mean surrendering control over the interesting parts, it means the boring, error-prone parts (provisioning, patch vetting, backups, per-channel credential wrangling) are handled so you’re not the one paying the operational tax for running a framework that changes shape every few weeks.
The Real Cost of Going It Alone
A bare VPS is cheaper on the invoice. That comparison only holds if you ignore the engineering hours that go into provisioning, ongoing patch tracking, and maintaining the underlying server. Once you price in actual incident response time against a framework that ships behavior-changing updates on a fast cadence, the math tends to flip faster than people expect.
That math doesn’t hold for everyone, though.
Teams that already have dedicated platform engineers treating agent infrastructure as a core competency rather than overhead, or teams building something intentionally nonstandard on top of these frameworks, are the exception here.
If that’s not you, and especially if you’re trying to keep an agent alive and improving in production for months rather than running it as a weekend project, you’re better off not carrying the state-loss risk, the patch-vetting burden, and the multi-channel single-point-of-failure risk yourself. That’s a lot of ongoing operational surface to own just to save the difference between a managed instance and a raw VPS, and it’s exactly the set of problems a platform like Cloudways Managed AI Agents was built to take off your plate for the long haul.
Self-Host or Managed: How to Actually Decide
Self-host when you need deep customization of the runtime itself, you already have infrastructure and security expertise in-house, or you’re doing agent framework development rather than just deploying one. In that case, the operational burden covered above isn’t overhead, it’s the main job of the team.
Go managed when you want to move from prototype to production quickly, you don’t have dedicated ops capacity to own patch vetting and backup strategy indefinitely, or you’re running agents across multiple clients or tenants and need consistent security handling without reinventing it per deployment.
If you’re still unsure which camp you’re in, the questions below map directly to the gaps this piece has walked through, so answer them honestly rather than aspirationally.
- Are you comfortable being the one debugging why a runtime update changed your agent’s behavior at 2 a.m.?
- Do you have a real backup strategy for accumulated memory and skills, not just code?
- Is losing every channel at once because one gateway process died an acceptable outage mode for you?
If any of those make you wince, that’s the signal to try Cloudways Managed AI Agents as your agent hosting platform. We invite you to use the promo code HACKERNOON while signing up to get $5 in Cloudways credits.
Conclusion
The six problems mentioned above are direct consequences of agents breaking assumptions that web app operations have depended on for over a decade. Managed hosting is one valid answer to that set of gaps.
As more agent frameworks show up beyond OpenClaw and Hermes, the question of who is validating updates before they hit production and who is protecting the state an agent has spent weeks accumulating is only going to matter more. That’s an infrastructure question, not an AI question, and it deserves the same rigor we’ve spent a decade applying to everything else we run in production.