He Replaced His DevOps Team with OpenClaw

0:00This guy just replaced his entire devops team with five open claw agents saving him thousands of dollars a month. And when I say replaced, he literally replaced them with five AI agents all running on a virtual machine and these agents monitor his servers. They debug issues, coordinate with each other, write documentation, and then they report back to him on telegram. All automated running 24/7 on a single four CPU eight gig of RAM virtual machine and it's working. That's the crazy part. This is one of those use cases that makes you sit back and really think about what is possible in 2026. So I'm going to break down exactly how this guy set the whole thing up because the architecture is actually really smart and really similar to how I'm

0:46running my AI agents. If you're running any kind of infrastructure or you manage servers for a business, this is going to be very relevant to you, which is why I make these videos for you on the other side watching this. If you want to implement some of this, give the link of this YouTube video to your open claw agent say, "Hey, I would like to do this. Let's figure it out." And a lot of the times we put the sources to all these use cases down in the description below. So this guy starts by creating what he calls the lead infrastructure engineer. This is the orchestrator agent. Think of it like the team lead, the project manager, the one who takes orders from you and delegates to everyone else. That's exactly how I have my whole AI agent team set up. So this guy connects to telegram so he could just message it from his phone. I love

1:32doing that as well. And if you think about that for a second, he's walking around, maybe he's at lunch at Taco Bell, maybe he's at the gym, maybe he's eating at McDonald's, whatever. He pulls out his phone, he opens telegram, and he says, "Hey, check what's going on in the database." Or, "Hey, what's going on with the containers today?" And the AI handles it. No laptop required, no VPN, no logging into dashboards, just a voice message. Here's where it gets more interesting. The lead engineer doesn't just do all the work itself. That would be like having one employee trying to do five jobs at once. Possible, but not probable. Instead, he has an entire team. So he created four specialized agents underneath it. Agent number one handles the MongoDB Atlas. This is the database specialist. It knows how to check connection pools, find slow

2:17queries, monitor storage, and all the MongoDB specific stuff. Agent number two handles the Azure container apps. This one monitors deployments, checks resource usage, looks at scaling configuration, and make sure the containers are healthy. Agent number three is the data dog agent. This is the monitoring specialist. It pulls metrics, looks for anomalies, checks for alert histories, and basically keeps an eye on everything from a higher level. Real quick, before we keep going, if you're watching this and you want to actually build with some of these tools, not just watch videos about them, then you're going to want to check out our community down below, Shipping School. We [music] have a full cloud code course, a full open claw course, and four live bootcamps every

3:03single week where we actually help you get set up from scratch. Like actually set this thing up, not just watch a tutorial and figure it out by yourself. And we also provide one-on-one coaching so you could book a call with me, we could share screens, and I can help you get cloud code or open claw running on your machine. That's it, no fluff. I built this community because watching YouTube's only gets you so far. We launched it just three days ago and we have over 55 members. You need people around you who are actually building, people who hold you accountable, and coaches who can help you when you get stuck. I'll put the link in the description down below. Get in now before the price goes up. The fourth agent is more of the technical writer. This is the one that takes all the analysis from the other agents and publishes it to confluence. Auto

3:49documentation, which is honestly the most underrated part of this whole setup. So when the engineer sends a message to the lead agent, here's what happens step by step. Let's say he messages, "Hey, the app seems slow. What the hell's going on? Can you look into it?" The lead agent receives the request and then breaks it down. It figures out which sub agents are needing to be involved. Then it spawns those agents with specific instructions for each one. So it might tell the Azure agent, "Check the container app. What is going on with the deployments? Look at the CPU and memory usage over the last hour. Check if the containers have restarted." It tells the data dog agent, "Pull the latest metrics. Look for any anomalies in the last 60 minutes. Check if any alerts have fired." And then it tells the MongoDB agent, "Check for slow queries. Look at the connection pool

4:34stats. See if there are any locks or high latency operations." Each one of those agents goes and does their investigation completely independently. You know, they're not waiting on each other. They're all working at the same time in parallel, talking to their respective APIs, gathering data, and then analyzing it. Then they each report their findings back to the lead agent. The lead agent takes all of that information, it cross-references it, it finds the patterns, and it sends a unified report back to the engineer on telegram. So instead of him having to log into MongoDB Atlas and then switching to the Azure pearl and then switching to the Azure portal, then going back to data dog, then trying to piece together what's going on, he basically instead of all that, he gets one clean summary, one message on telegram. Here's what's happening.

5:20Here's what's probably causing it. And here's what we recommend to fix it. It's not just reactive, it's part of what this that really got me. It's like he set up cron jobs for automated check-ins midday and the end of the day, twice a day without him basically doing anything. And the agents automa- the agents automatically do a full health check of his entire infrastructure. They check every service, every metric, every database, and they compile it into a nice little report for him to go over. Then after that, the technical writer agent gets that report and it publishes it to confluence. So his team documentation is always up to date and it's always current without anyone having to write a single word. Now what I want to talk about is that technical writer agent for just a second because I

6:06think this is probably the most valuable part of the entire setup. Documentation is one of those things that everyone knows is important, but nobody wants to do it. How many times have you fixed a bug at 2:00 in the morning, got it working, and then just went to bed without writing it down and what you did? I've done it. You've done it. Everybody does it. And then 3 months later that same bug comes back and you're sitting there like, "What did I do last time?" And nobody remembers because nobody wrote it down. This guy eliminated that problem completely. Every investigation, every health check, every analysis gets basically automatically documented to confluence. That's not just convenient, that's building institutional knowledge for your app. Even if he leaves the company tomorrow, all of that knowledge is captured and organized and it's

6:52searchable. That's the value in this. So I'm going to talk about the bigger picture here because this isn't just a cool tech demo, this actually has real business implications. Devops engineers are very expensive, usually $100,000 a year full-time. And I looked into the numbers. That's the average. Basically around $140,000 a year is basically the American average. Senior devops, you're looking at about $180,000 a year. Staff level, $200,000. Yeah, not cheap. And most companies don't have just one devops engineer, they have teams, like two or three people, five people. Some have 10, 50. They're monitoring deployments, they're managing the infrastructure, incident response, all the documentation. This guy is doing a

7:37significant chunk of that work with five AI agents on a four CPU eight gig virtual machine. That's crazy. So if you do the math on what this costs, a virtual machine that's on Azure or AWS is maybe 40 to 60 bucks a month. API costs for the models he's using, let's say cloud sonnet 46 and quin 3.5, which are more mid-tier and open source. So his total token costs are probably a couple hundred dollars a month depending on usage. Let's be generous and just say the whole thing cost him like $300 a month. Compare that to even one junior devops engineer at $100,000 a year. That's over $8,000 a month in salary alone. And they're probably not as good. Not counting the benefits, equipment, office space, management, and training, and all that overhead that goes into it.

8:22This guy's basically running five specialized agents for $300 a month. And it's better. Now I'm not saying these agents replace a senior engineer. They don't, but they extend one engineer's capabilities dramatically. One person plus five agents can cover ground that would normally take a three-person team. That changes the economics completely. And this connects something that I've been preaching on this channel for at least four weeks. The future isn't AI replacing humans. Get that out of your mind. It's extending humans. One person with the right agent setup becomes an entire team. A small company with agents become competitive with much larger company. That's the unlock and that's what open claw enables. Let me talk to you about how this connects to what I'm actually building since it's called the Build in Public channel because I do run a similar pattern for my content

9:07operation. I've got Atlas as my orchestrator agent. Ghostface handles the daily intel and research. The Rizza writes the script outlines. Inspector Deck manages everything on X. The Jizza does the newsletters and blog. But each one has a specific domain and specific tools, specific responsibilities, and they are coordinated through me as the hub. Same principle, different domain. Devops or content creation, basically the architecture is very similar. The orchestrator that manages the specialized sub agents and all that can fall in line with what I've been talking about. If you're struggling to keep up with content, I'm about to save you about 40 days worth of work. I built something [music] called content machine. It's 10 AI agents that run on the open claw orchestration and they

9:52handle everything. Scripts, thumbnails, X posts, blogs, outreach, clips, news leaders, all of it. So, I went from 1,000 subscribers to 4,000 subscribers on YouTube in 7 days using this exact system. Every single morning, I wake up and the content's already done. I spend [music] maybe 15, 20 minutes reviewing and approving them and I move on with my day. [music] It works for any niche, fitness, finance, real estate, marketing, whatever you are building and it is 100% completely customizable to your use case. So, you get the mission control dashboard, all of the cron jobs, everything I've built over the last 40 days helping me gain more and more people to subscribe and join the

10:37community. So, you plug in your own thing and it molds it to you, it learns how you talk and it writes so it doesn't sound like AI slop. $97 one time, it's not a subscription. I'll put the link down below and you'll thank me later. The beauty of OpenClaw is that this pattern works for literally anything. You can build a virtual team for customer support, for sales outreach, for marketing, for legal document review, for financial analysis. You just define the roles and give each agent the right tools and context, the credentials, and set up the communication flow and just let them work. You know, the cron scheduling piece is a huge one too because that means your agents aren't just sitting there waiting for you to ask a question. They're proactively monitoring, proactively checking, proactively documenting. They're doing the work

11:23while you sleep, while you eat at Taco Bell, while you're spending time with your family. That's not just a chatbot, that's an employee and that's an entire team that works 24/7 for the cost of a nice dinner every month. This guy's next step is connecting the whole system to Asana so he can drive proper task management through the agents instead of just Telegram conversations. I love that because once you go from conversational commands to structured task workflows, the reliability goes way up. You get tracking, prioritization, history, and all that project management stuff that just makes operations much more smoother. That's the natural evolution of this. You start with a chat-based command because it's easy and fast, then you trust the system more, you move to

12:08structured workflows because they're more reliable and auditable. He's doing exactly the right progression. And I also want to point out the model choices he made because I think that's very smart. He's running Sonnet 4.6 for the complex analysis work and then Claude 3.5 for the simpler like monitoring tasks. That's model routing in action. He's not paying Opus prices for routine health check that just needs to read some metrics and flag anything unusual. He's saying he's saving the expensive model for the task that actually need it. That's how you keep costs reasonable when you're running five agents around the clock. Here's what I want you to think about this if you're just watching it and you want to implement something similar. I want you to think about what team do you wish you had? What tasks are eating up every time and every single day that could be broken down into specific specific agent roles? Maybe

12:54you're a solo founder doing customer support and marketing and operations all by yourself. Like what if you had three agents handling the routine stuff so you could focus on the work that actually moves the business forward? Maybe you're an engineer who spends half of your day in dashboards and the other half in meetings. Let's just say what if an agent did the dashboard part and just texted you the summary? Wouldn't that be nice? That's the question I want you to ask yourself. The answer is OpenClaw because this isn't some future vision, this isn't a research paper, this is a guy doing it right now, a solo engineer with five agents on one virtual machine. Full infrastructure monitoring and documentation on autopilot. If you want to learn how to set this type of stuff up and run your own business exactly like this, your own projects, your own life, it's exactly what we do inside the community, inside Shipping School. We've

13:39got 114 members in the last 11 days creating things just like this, systems just like this. We use six boot camp calls every single week. Our whole point is hands-on approach, group-like setting. We take you to the next step in your building process. We're real people building real setups that are providing real results. So, you could get hands-on help and we'll help you get OpenClaw set up correctly so you don't spend $500 a month with proper improper model routing. We'll get you set up with Claude code so you could get your startup off the ground. We also do a social media call every Saturday to help you get your first customers through content creation. I'll put the link in the description down below. Get in before the price goes up. We do four to six videos every single day on this YouTube channel. If you like keeping up with the AI news, make sure you hit

14:25subscribe and we'll see you in the next one. Bye-bye.