What Is an AI Agent and How Do You Actually Run One?

Key Takeaways

• An AI agent gathers information, reasons, and takes action on its own, unlike chatbots or fixed automation tools.
• Every agent runs the same cycle, perceive, reason, act, observe, until the task is done.
• Running an agent has three real costs, the server, the LLM API, and your own maintenance time.
• Where an agent runs matters, unmanaged setups carry real security risks that managed hosting removes.

AI agents can schedule your meetings, keep tabs on competitors, triage emails, and write code for you while you sleep.

Most people have heard this by now. What they haven’t uncovered is how any of it actually works, how much it costs to run, and what happens when something goes wrong.

This is what this guide is about. We’ll cover:

• What an AI agent is and how it differs from the tools you already use
• How they work under the hood
• What they cost to run and where the hidden expenses are
• Where the real risks are
• How to deploy one without the common first-timer mistakes

By the end, you’ll know if an AI agent is right for you, which one to pick, and how to get it running.

What Is an AI Agent?

An AI agent is a software program that can gather information, make decisions, and take real actions to achieve a goal, without being explicitly told how to do every step.

That last part is what makes it different from everything else you’ve probably used.

A calculator does exactly what you tell it. An accountant, on the other hand, can look at your financial situation, figure out what needs to happen, and make judgment calls along the way.

An AI agent is closer to the accountant.

It gathers information from the world around it, figures out what to do with that information, and then actually does something about it.

How an AI Agent Differs From a Chatbot

A chatbot is designed to wait for your input and respond to it. Chatbots are genuinely useful for handling FAQs, automating support processes, and walking users through forms and wizards.

However, chatbots are rule-based and cannot operate outside of whatever rules they were built with. They also don’t retain anything from previous conversations, so every interaction starts from scratch.

Whereas a chatbot waits for you to tell it what to do, an AI agent can act on its own to accomplish a goal. It carries out tasks based on the information available to it, without needing you to prompt it at every step.

A chatbot is basically a very smart answering machine. An AI agent is more like an assistant who can actually get things done without you standing over it.

How an AI Agent Differs From Automation Tools Like Zapier and Make

Zapier and Make are powerful automation tools, and they’re genuinely good at what they do. They can connect your email to a spreadsheet so every client message gets logged automatically, or fire a Slack notification the moment someone fills out a form on your website.

For repetitive, predictable tasks, they work incredibly well.

However, Zapier and Make are very rigid. If something happens that doesn’t match the rules you set up, the automation will either fail or, at best, do the wrong thing without telling you.

There’s no judgment happening, just rule-following.

An AI agent doesn’t follow a fixed script. Instead, it reads what’s in front of it and decides what action makes sense given the context. Similar goals to what Zapier does, but achieved through an entirely different approach.

How an AI Agent Differs From an AI Assistant

Ask an AI assistant, “How can I increase my sales?” and it will give you a solid list of ideas. Ask it to schedule a meeting, and it will tell you exactly how to schedule one.

However, it won’t actually book the meeting for you. It provides the knowledge, but stops there.

An AI agent is much more hands-on. It connects to your calendar, your inbox, your browser, and whatever else you give it access to, and it keeps working whether you’re watching or not.

An AI assistant stops the moment you close that tab. An AI agent keeps going.

Now that we’ve covered what an AI agent is and how it differs from the tools you’re already using, the next question is how it actually works under the hood.

How Do AI Agents Work?

Understanding what an AI agent does is one thing. Understanding how it works is what separates people who deploy agents successfully from people who end up with broken workflows and unexpected bills.

The Perception-Reasoning-Action Loop

Almost every AI agent today runs on the same core cycle, commonly called the ReAct loop (Reasoning and Acting). It works like this:

1. Perceive: The agent takes in the current situation: your goal, recent tool results, and context from previous steps
2. Reason: It decides what to do next
3. Act: It calls a tool or takes an action
4. Observe: It looks at what happened and feeds that back into the next cycle

This repeats until the task is done or a stopping condition is reached. Multi-agent systems apply the same loop, just spread across several agents working in parallel instead of one.

A good way to think about it: a chef working through an unfamiliar recipe doesn’t cook the whole dish at once. They try a step, taste the result, adjust, and try again. An AI agent works the same way, looping through the process until it gets to the finish line.

How AI Agents Work Through Complex Tasks

When a goal is too large to act on directly, the LLM at the core of the agent breaks it down into smaller sub-tasks. Each sub-task becomes its own starting point for the ReAct loop.

Ask an agent to research a competitor and produce a report, and it won’t just generate a report.

The LLM figures out what steps are needed and the agent works through them in sequence, searching for recent news, checking their product pages, pulling pricing information, and only then starting to write.

That ability to plan and adjust mid-task is what separates agents from static automation tools. A Zapier workflow can’t decide halfway through that the plan needs to change. An agent can.

Short-Term vs Long-Term Memory in AI Agents

Memory is one of the most important and least understood parts of how agents work.

Short-term memory lives inside the context window, which is the amount of text the model can process at once. Everything the agent needs right now lives here. When the session ends, it’s gone.

Long-term memory is what lets an agent persist knowledge across sessions. It stores things like your past conversations, your preferences, and learned skills that the agent can draw on the next time a similar task comes up.

Without long-term memory, every session starts from scratch. That’s why most basic AI assistants feel frustrating to use over time, and why agents built around persistent memory, like Hermes, feel fundamentally different after a few weeks of use.

Latency and Performance in Multi-Step Workflows

AI agents are slower than they appear. Every step in the ReAct loop has two sources of delay.

One is the time it takes the model to reason and respond, and the other is the time it takes to call an external tool and wait for a result.

Multiply those delays across dozens of steps, and a task that feels like it should take seconds can easily take minutes. According to LeanOps production benchmarks, AI agents burn roughly 50x more tokens than single-turn chatbots on equivalent tasks.

For background tasks like overnight monitoring or document processing, this usually isn’t a problem. For anything time-sensitive where a user is waiting, it’s something to design around carefully.

Stop Reading. Start Running One.

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How Do AI Agents Connect to Tools and Services?

An AI agent that can only think but not act is just a slow chatbot. What makes agents genuinely useful is their ability to reach into the real world and do things.

That’s where MCP comes in.

What Is MCP and Why It Matters in 2026

Before MCP, every AI tool needed its own custom integration for every service it wanted to connect to. Cursor needed its own Notion connector. Claude Desktop needed a different one. Multiply that across hundreds of tools and thousands of services, and you have a maintenance problem that doesn’t scale.

Model Context Protocol is the open standard that fixed this. Anthropic released it in November 2024 and donated it to the Linux Foundation in December 2025. By 2026, every major AI platform supports it, including Claude, ChatGPT, Gemini, Cursor, and VS Code Copilot.

The best analogy is USB-C. Before USB-C, every device had its own cable and nothing worked with anything else. After USB-C, one standard works everywhere. MCP does the same thing for agents and the tools they need to reach.

Build an MCP server once, and any compatible agent can use it, regardless of which LLM it runs on.

How MCP Makes Agents More Powerful

Without MCP, an agent is limited to whatever was built into it at the start. With MCP, it can connect to any system that exposes an MCP server, and the list grows every week.

Cloudways MCP is a good example.

Connect your AI tool to Cloudways once, and you can manage your entire hosting fleet through conversation. Spin up servers, deploy from Git, run malware scans, manage SSL, and handle client billing, all without opening the dashboard.

The agent stops being a tool inside a chat window and starts being the interface through which you control everything else.

Your Hosting, One Prompt Away

Connect your AI tool to Cloudways and manage your entire hosting setup through conversation.

Explore AI Agents

Types of AI Agents

Not all AI agents are built the same way. The type you choose determines how capable it is, how much it costs to run, and how well it holds up under pressure.

Single Agents vs Multi-Agent Systems

A single agent does everything itself. One reasoning loop, one set of tools, one goal at a time. For most use cases this is exactly what you need, and considerably easier to manage than running multiple agents in parallel.

A multi-agent system is a team of specialized agents working in parallel. One handles research, another handles writing, a third reviews the output before anything goes out.

According to IABAC research, multi-agent systems outperform single agents by over 90% on complex tasks.

But that performance gain comes with significantly more complexity, cost, and maintenance. For most use cases, a single agent is the right starting point. Move to multi-agent only when the task genuinely requires it.

Task-Specific vs General Purpose Agents

A task-specific agent is optimized for one job. A customer support agent. A code review agent. A competitor monitoring agent. It’s faster, more reliable, and far less likely to behave unexpectedly.

A general purpose agent handles a wider range of tasks but without the same reliability or speed. It’s better suited for exploration than for anything running in production.

Most serious deployments use task-specific agents for production workflows and general-purpose agents for exploratory work.

Self-Learning Agents

Most agents are static. You configure them once, and they stay exactly as you set them up.

A self-learning agent improves over time based on what it actually does. Hermes is built around this. Every time it completes a task it hasn’t seen before, it writes the solution as a reusable skill. The next time something similar comes up, it draws on that skill automatically.

After weeks of use, the agent on your account is measurably more capable than the one you started with, all without any reconfiguration on your part.

This is also why hosting matters more than most people expect. An agent building its own skill library needs infrastructure that protects those skills through updates. Cloudways validates every Hermes update before deployment, specifically to prevent a bad release from wiping out weeks of learning.

What Can You Use an AI Agent For?

The answer depends on what you actually do every day. Here’s what agents are doing for the four main audiences Cloudways serves.

AI Agents for Developers

The biggest time drain for most developers isn’t writing code. It’s everything around it. Setting up environments, running tests, managing deployments, monitoring builds, and responding to alerts that fire at inconvenient hours.

An agent handles all of this. Connected to your Git repository, it monitors open PRs, runs test pipelines, flags failures, and sends you a summary of what needs attention.

For developers on Cloudways, the MCP integration turns your AI tool into a direct interface to your entire hosting fleet. Describe what you need and it handles the rest.

AI Agents for Agencies

Most agency work is repetitive. Weekly reports, status updates, performance monitoring, answering the same client questions over and over.

An agent takes most of that on. It monitors client site performance, flags anomalies before the client notices, drafts status updates in the agency’s voice, and handles routine communications without anyone on the team getting involved.

For agencies on Cloudways, agents can manage server configurations, run security scans, and handle billing across multiple accounts, all from a single conversation.

AI Agents for Ecommerce Store Owners

The highest-stakes moments for any store are the ones you least want to be caught off guard by. A traffic spike during a campaign. A competitor dropping prices. A product going viral overnight.

An agent monitors all of this. It tracks competitor pricing, flags performance issues during traffic spikes before they affect conversions, and handles inbox triage after a big promotion.

The stores that handle peak seasons well have usually set up systems that work while they’re not watching.

AI Agents for Small Businesses

Small businesses often have one person handling sales, support, operations, and marketing, sometimes all on the same day.

An agent doesn’t replace that person. It takes the repetitive work off their plate. Inbox triage, appointment scheduling, competitor monitoring, recurring reports, and customer follow-ups all run in the background.

The decision-making stays with the human. The groundwork doesn’t have to.

The Leading Open-Source AI Agents in 2026

If you’ve decided an AI agent is right for you, the next question is which one to run.

Two names come up more than any others right now. OpenClaw and Hermes. Both run on Cloudways. Both are capable. But they work very differently.

What Is OpenClaw?

OpenClaw started as a side project by Austrian developer Peter Steinberger and was published to GitHub in November 2025 under the name Clawdbot. It went through two name changes before settling on OpenClaw in January 2026.

By mid-2026, it had accumulated over 346,000 GitHub stars, more than any runnable software project in GitHub’s history.

The reason it spread so fast is that it actually does things. You message it on WhatsApp or Telegram, and it runs commands, manages files, browses the web, handles email, and executes multi-step tasks without you staying involved.

It runs 24/7 on your own infrastructure, connects to over 1,700 community-built skills, and works with any major LLM through your own API keys. Steinberger has since joined OpenAI to work on personal agents, while OpenClaw moved to an independent foundation and remains open-source under the MIT license.

Running it yourself means you own everything. The setup, the security, the updates, and the maintenance. That’s manageable for developers but a real overhead for everyone else.

OpenClaw, Without the Setup Headache

Skip the Docker, the SSL certs, and the 3am update alerts. Deploy OpenClaw on managed infrastructure instead.

Launch OpenClaw

What Is Hermes Agent?

Hermes is built around learning rather than configuration. Where OpenClaw relies on a skill ecosystem you set up yourself, Hermes creates its own skills from the tasks it completes. The longer it runs, the more capable it becomes without you having to do anything extra.

Every task it completes that it hasn’t seen before gets written as a reusable skill, stored on your instance, and drawn on automatically the next time something similar comes up.

It also maintains persistent memory across every conversation, so it always knows the context of your projects without you having to re-explain anything. Hermes supports Telegram, Discord, Slack, and WhatsApp out of the box, with one memory across all channels.

An Agent That Gets Smarter Every Week

Hermes builds its own skills as it works. Get it running on Cloudways and let it start learning your workflows today.

Launch Hermes

OpenClaw vs Hermes: Which One Is Right for You?

The decision is mostly about how much you want to manage.

OpenClaw gives you more control. You install the skills you want, configure the integrations yourself, and decide exactly what the agent can and can’t do. It suits developers who want to customize deeply and understand exactly how the agent behaves.

Hermes handles more of that for you. It builds its own capabilities over time, needs less ongoing configuration, and gets more useful the longer it runs. It suits agencies, solopreneurs, and small businesses who want an agent that works well without requiring constant attention.

Both run on Cloudways Managed AI Agents on the same infrastructure, with the same security handling, LLM integrations, and channel support. You can run both on the same account if your workflows call for it.

How Much Does It Cost to Run an AI Agent?

Most people underestimate the true cost of running an AI agent. Not because the numbers are hidden, but because there are three separate costs and most guides only mention one.

Infrastructure Costs

Where your agent lives determines your baseline cost.

Setup	Monthly Cost	The Catch
Your laptop	Electricity only	Agent stops when laptop sleeps or loses internet
Self-hosted VPS	$5 – $20/mo	You manage all updates, security, and maintenance
Managed hosting	From $9.99/mo	Provider handles the server and ops for you

For anything that needs to run around the clock, a laptop is not a practical option. The real decision is between a VPS you manage yourself and managed hosting, where someone else handles it.

LLM API Costs and How BYOK Works

The server keeps the agent running. The language model is what it thinks with, and that cost is billed separately by your LLM provider.

BYOK stands for Bring Your Own Key. You create an API key with OpenAI, Anthropic, or Google, connect it to your agent, and pay that provider directly at their standard rates. Cloudways never touches your key. Your usage goes straight to your provider.

What you pay depends entirely on which model you pick.

Provider	Model	Input per 1M tokens	Good for
OpenAI	GPT-4o mini	$0.15	Routine tasks, high volume
Google	Gemini 3.1 Flash-Lite	$0.25	Routine tasks, high volume
Anthropic	Claude Haiku 4.5	$1	Balanced performance
Anthropic	Claude Sonnet 4.6	$3	Complex tasks
Anthropic	Claude Opus 4.8	$5	Most demanding work

Most people running agents in production route routine tasks to a lighter model like GPT-4o mini or Gemini Flash-Lite and reserve Claude Sonnet or Opus for work that genuinely needs deeper reasoning.

The cost difference between GPT-4o mini at $0.15 and Claude Opus 4.8 at $5 per million tokens is over 33x, so choosing the right model for each task is worth thinking about before you start.

The Hidden Cost Nobody Talks About

The server bill and the API bill are visible. What never shows up on an invoice is your time.

Self-hosting means you own every update, every security patch, and every broken dependency. OpenClaw shipped 15 releases in 19 days during one stretch in 2026. Keeping up with that while running a business is a real job.

Here’s what the math actually looks like:

Cost	Self-hosted	Managed (Cloudways)
Server	$15/mo	$9.99/mo
LLM API	$20/mo	$20/mo
Maintenance (10hrs × $100/hr)	$1,000/mo	$0
Total	$1,035/mo	$29.99/mo

Source

The VPS looks cheap until you count what it actually takes to keep it running.

What Cloudways Charges for OpenClaw and Hermes

Four tiers. You pay for the server. LLM usage is billed directly by your provider.

Plan	vCPU	RAM	Storage	Price	Best For
Scout	1	2GB	50GB	$9.99/mo	Individuals, light use
Operator	2	4GB	80GB	$19.99/mo	Small teams
Squad	4	8GB	160GB	$39.99/mo	Agencies, multiple workflows
Swarm	8	16GB	320GB	$79.99/mo	Heavy production workloads

Every plan includes automated daily backups, managed SSL, validated updates, and SSH access. Billing is hourly. The price you start on stays the same at renewal.

Is It Safe to Run an AI Agent?

It depends entirely on how you run it. Let’s learn more about this:

The Risks of Running an Agent on Your Laptop or an Unmanaged Server

An AI agent connected to your email, your files, your calendar, and your browser has deep access to your digital life. That’s what makes it useful. It’s also what makes the question of where it runs so important.

When an agent runs on your personal machine, it shares an environment with everything else on that machine. If something goes wrong through a malicious skill or a bad software update, your passwords, documents, and credentials are all at risk.

Running it on an unmanaged server puts some distance between the agent and your personal data. However, you then own every security decision. Authentication, SSL, firewall rules, software updates. Miss one and you’re exposed.

What the OpenClaw Security Crisis Revealed

In early 2026, OpenClaw went from the fastest-growing open-source project in GitHub history to the subject of urgent security advisories from Microsoft, Google, Meta, and CrowdStrike. Here’s what happened, in order:

January 2026

Researchers discover CVE-2026-25253, a critical vulnerability in OpenClaw’s gateway. No credentials needed to exploit it. A single malicious link sent to the agent executes code on the host machine.

February 2026

135,000+ exposed instances found across 82 countries. 63% with no authentication at all. Over 800 malicious skills uploaded to ClawHub disguised as Gmail, Notion, and GitHub integrations, silently harvesting passwords and browser cookies from anyone who installed them.

March 2026

Nine CVEs disclosed in four days, including one rated 9.9 out of 10 in severity. Belgium’s cybersecurity authority issues an emergency advisory.

This didn’t happen because of sophisticated attacks. It happened because most people were running a powerful, deeply connected agent on unmanaged infrastructure with default settings and no monitoring.

How Managed Hosting Reduces the Risk

Managed hosting doesn’t make an agent completely immune. But it removes the conditions that caused the most damage in 2026.

On Cloudways, every agent runs on its own dedicated instance, completely separate from other customers. The agent only has access to what you explicitly give it.

Every update is tested before it reaches your instance. A bad release gets held back, not pushed automatically. SSL is managed. Daily backups run on a schedule. If something breaks, you restore from the dashboard.

The 135,000 exposed instances were almost all self-hosted with no authentication and no monitoring. That’s the gap managed hosting is built to close.

Agent Boundaries and Governance

Giving an agent access to your tools and data is the point. It’s also where things can go seriously wrong if you haven’t thought about what happens when the agent does something you didn’t intend, or gets stuck doing something you can’t afford.

What Is Human-in-the-Loop and Why It Matters

Most people treat human-in-the-loop as a switch. Either the agent runs on its own or a human approves everything it does. That framing misses the point.

Human-in-the-loop is a design decision you make action by action. Some things an agent does are low-risk and reversible. Reading an email. Searching the web. Pulling a report. These don’t need approval. Other things are high-risk and hard to undo. Sending an email to a client. Deleting a file. Pushing code to production. These probably do.

The goal isn’t to review everything. That defeats the purpose of having an agent. The goal is to put approval gates on the actions that matter and let the agent run freely on the ones that don’t.

How to Set Limits So Your Agent Doesn’t Overstep

An agent without boundaries will eventually do something you didn’t expect. Not because it’s malicious but because it’s optimizing for the goal you gave it, and goals can be achieved in ways you didn’t anticipate.

The practical approach most teams use comes down to four controls:

Define what the agent can access.

Give it only the tools and data it needs for the task. An agent handling email triage doesn’t need access to your file system.

Set action boundaries.

Specify what it can do without approval and what requires a human sign-off. Write these down explicitly rather than assuming the agent will figure out where the line is.

Build an audit trail.

Every action the agent takes should be logged with enough context to reconstruct what happened and why.

Test before you trust.

Run the agent on low-stakes tasks before giving it access to anything critical. Guardrails AI and NVIDIA NeMo are two widely used tools for enforcing these boundaries programmatically.

Token Burn: What Happens When an Agent Gets Stuck and How to Prevent It

This is the failure mode nobody warns you about until it’s too late.

When an agent gets stuck, it doesn’t just stop. It keeps trying. Every attempt sends the full accumulated conversation history back to the language model as input, which means each loop gets more expensive than the last. A 100,000-token conversation costs 100,000 input tokens on every subsequent call, regardless of how little new content you add.

One widely cited incident involved two agents ping-ponging requests between each other for 11 days, generating a $47,000 bill. Another team came back after a long weekend to find their agent had burned $4,200 overnight. All their monitoring dashboards were green the entire time.

Three things that catch runaway agents before the bill arrives:

• A daily spend cap. A $10/day hard cap at the API gateway catches 95% of runaway incidents before they become crises.
• A loop detector. Monitor token consumption rate rather than total. A healthy agent pauses to call tools and wait for results. Sustained high-velocity token consumption with no task progress is the signature of a stuck agent.
• A kill condition. Define explicitly what trips the shutdown and what happens when it does. Make these hard stops, not logged warnings.

How to Run an AI Agent: Your Three Options

You’ve picked your agent. Now you need somewhere to run it. The option you choose affects everything from how much time you spend on maintenance to whether your agent is still running when you wake up tomorrow.

Running It Locally on Your Machine

Local is where most people start. You install the agent software on your laptop or desktop, connect your API keys, and start chatting. No server required. No monthly bill beyond your LLM usage.

The setup is relatively simple for OpenClaw. Install Node.js, download the agent files, add your API key, and run the start command. Most people have it working within an hour.

The problem shows up later. Your agent stops the moment your machine sleeps, loses internet, or needs a restart. It can’t monitor your inbox at 3 am if your laptop is closed. It can’t respond to a Slack message while you’re on a flight. For anything that needs to run continuously, local is a starting point, not a solution.

It also puts your agent in the same environment as everything else on your machine. Your files, your passwords, your browser sessions. If something goes wrong with the agent, everything it can reach is at risk.

Good for testing and learning. Not practical for anything that needs to run around the clock.

Self-Hosting on a VPS

A VPS, or virtual private server, is a rented computer that lives in a data center and runs 24/7. You install the agent software on it, configure it, and it keeps running regardless of what your personal machine is doing.

The setup is more involved.

Depending on your configuration, you may need to install Docker (software that packages applications so they run consistently), set up an SSL certificate, configure security settings, and make sure the agent restarts automatically if the server reboots.

For developers comfortable with Linux, this is manageable. For everyone else, it’s a steep learning curve.

The ongoing maintenance is where most people underestimate the commitment. Agent software updates frequently.

Every update needs testing before you apply it, because a bad one can break your agent’s learned skills or integrations. Security patches need to go on quickly. You also need monitoring so you know when something breaks.

For developers who enjoy this kind of work, self-hosting is a reasonable choice. For everyone else, it’s a second job that comes with the territory.

Good for developers who want full control and don’t mind the maintenance that comes with it.

Managed Hosting on Cloudways

Managed hosting removes the infrastructure layer entirely. You pick a plan, connect your API keys, and your agent is running on dedicated infrastructure within minutes.

On Cloudways Managed AI Agents, every OpenClaw and Hermes instance runs on its own dedicated server, completely separate from other customers.

Updates are validated before they reach your instance. SSL is handled automatically. Daily backups run on a schedule. SSH access is included on every plan for anyone who wants direct server access.

Getting started takes three steps. Create your Cloudways account, connect your LLM API key, and start chatting. Most people are running their first task within ten minutes.

The tradeoff is that you don’t control the underlying server configuration. For the vast majority of users, that’s never been a need anyway.

Good for agencies, solopreneurs, SMBs, and developers who want a capable agent without the operational overhead.

How the Three Options Compare

	Local	Self-Hosted VPS	Managed (Cloudways)
Setup time	Under an hour	2-4 hours	Under 10 minutes
Runs 24/7	No	Yes	Yes
Maintenance required	Minimal	High	None
Security responsibility	You	You	Cloudways
Monthly cost	LLM costs only	$5-20 + LLM	From $9.99 + LLM
Best for	Testing	Developers	Everyone else

Your First Agent Is Ten Minutes Away

Pick OpenClaw or Hermes, connect your API key, and give it a task. No server setup required.

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How to Choose the Right AI Agent

The right agent isn’t necessarily the most capable one. It’s the one that fits how you actually work and how much you’re willing to manage.

Questions to Ask Before You Decide

Before you look at any feature list, answer these four questions honestly.

How technical are you?

Not whether you can learn something new, but what you actually enjoy doing. Do you like configuring software, troubleshooting issues, and staying on top of updates? Or would you rather focus on what the agent does and let someone else handle how it runs?

How much time do you have for maintenance?

Self-configured agents need ongoing attention. Skills need installing and updating. Integrations break. New versions ship. If your honest answer is “not much,” that should carry a lot of weight in your decision.

What does your budget look like?

Both agents run on the same Cloudways pricing tiers, so the server cost is the same. The difference is in the time cost. OpenClaw requires more setup and maintenance. Hermes requires less. Factor that in alongside the monthly bill.

How long are you planning to run it?

An agent you run for a few weeks to test something is a different decision from one you’re building your workflow around. The longer it runs, the more the self-learning advantage of Hermes compounds into something meaningful.

When OpenClaw Makes More Sense

OpenClaw suits you when control matters more than convenience.

If you want to understand exactly what your agent is doing at every step, OpenClaw gives you that visibility. You install the skills you want, configure the integrations yourself, and nothing happens without your input. You own the maintenance, the updates, and the security, but you also own every decision about how it behaves.

The community skill ecosystem is also a genuine advantage. Over 1,700 community-built skills cover everything from GitHub integrations to research workflows, making it the broadest library of pre-built agent functionality available right now.

OpenClaw works best for developers and technically inclined users running specific, well-defined workflows who want full visibility into how the agent behaves.

When Hermes Makes More Sense

Hermes suits you when you want the agent to get better without you having to make it better.

The self-learning loop is what sets it apart. Every task Hermes completes that it hasn’t seen before gets written as a reusable skill. The next time something similar comes up, it draws on that skill automatically. Over weeks of use, the agent becomes measurably more capable at your specific workflows without any additional input from you.

This makes Hermes particularly well-suited for agencies running it across multiple client workflows, solopreneurs who want a capable agent without ongoing maintenance, and small businesses where the person running the agent isn’t necessarily technical.

It also works better when your work is varied and hard to define upfront. If you’re not sure exactly what tasks you’ll want the agent to handle, Hermes figures that out as it goes. OpenClaw needs you to know what you want before you start.

And if you genuinely can’t decide, you don’t have to. Both agents run on the same Cloudways account, on the same infrastructure, with the same billing. You can run OpenClaw for development workflows and Hermes for everything else, without any additional setup.

How to Get Started With an AI Agent on Cloudways

If you’ve made it this far, you have everything you need to make a decision. Here’s how to go from reading to running.

If You’ve Chosen OpenClaw

1. Log in to Cloudways and click AI Agents, then My Agents

2. Click Add First AI Agent, name your instance, and select OpenClaw
3. Choose the region closest to you and continue

4. Pick your instance size, then connect your LLM provider (Anthropic, Google AI, OpenAI, DigitalOcean, or Openrouter) with your API key
5. Accept the terms and click Deploy Agent

6. Once it’s live, connect a messaging channel from the dashboard if you want to chat with it on WhatsApp, Slack, Discord, or Telegram. This step is optional, you can also just use the OpenClaw dashboard directly

7. Give the agent its first task

Start with one workflow you currently do manually. Something repetitive. Something that happens on a regular schedule. Get that working before you expand.

If You’ve Chosen Hermes

1. Log in to Cloudways and click AI Agents, then My Agents
2. Click Add First AI Agent, name your instance, and select Hermes
3. Choose the region closest to you and continue

4. Pick your instance size, then connect your LLM provider with your API key
5. Accept the terms and click Deploy Agent

6. Once it’s live, connect a messaging channel, Telegram, Discord, Slack, or WhatsApp, if you want to chat with it there

Hermes doesn’t need much configuration upfront. The skills it builds from your first few weeks of use will shape how capable it becomes over time. The more you use it, the more useful it gets.

If You’re Not Sure Which One To Pick

Start with Hermes. It requires less upfront configuration, learns from what you give it, and works well for most use cases straight away. You can always add OpenClaw to the same account later if you need the deeper customization it offers.

Conclusion

AI agents are not a future technology. They’re running in production right now, handling the work that used to require a person, a team, or a tool for every single task.

The gap between people who are getting real value from agents and people who are still figuring them out comes down to one thing: starting. Not with the perfect setup. Not with every question answered. Just with one task, one agent, and enough curiosity to see what happens next.

You now know more about AI agents than most people who are already running them. The rest you’ll figure out by doing.

If you’re ready to start, Cloudways Managed AI Agents is where most people in the Cloudways community begin. OpenClaw if you want control. Hermes, if you want something that learns. Both if your workflows call for it.