Building a 24/7 AI Automation System with OpenClaw: From Zero to Running
Building a 24/7 AI Automation System with OpenClaw: From Zero to Running
Here’s the thing about 24/7 automation: most people talk about it, very few actually build it. This is what happened when we decided to.
The Starting Point: February 5, 2026
We started from scratch. No existing system, no “proven playbook,” no fancy infrastructure. Just two things:
- A Mac mini (24GB RAM, local)
- A vision: Run an AI company on autopilot while we sleep
The first question wasn’t “How do we make money?” It was harder: “How do we keep this thing running without melting our wallet or our sanity?”
Three Core Principles That Saved Us
1. Move the Brain, Not the Muscle
We decided early: use cloud APIs for thinking (Claude, GPT), use local machine for execution (ffmpeg, scripts).
Why? Because:
- ❌ Running Ollama locally for inference = slow + expensive power bill
- ✅ Running Claude in the cloud = fast + pay per use only
- ✅ Video processing locally = free (after buying the hardware once)
This single decision shaped our entire architecture. Instead of a $500/month GPU server, we’re on GitHub Copilot ($10/month).
2. Multi-Model Strategy
Not all tasks need Opus. Not even most of them.
We built an automatic escalation system:
- gpt-5-mini: Simple tasks (trending, SEO checks, summaries)
- Claude Haiku: Light work (1-2K tokens, fast)
- Claude Sonnet: Content creation (blogs, ebooks, 2-5K tokens)
- Claude Opus: Architecture, complex reasoning, 10K+ tokens
The key: Start cheap, escalate only when needed.
On day 11, we caught ourselves running everything on Opus. One switch to Haiku for simple tasks cut our token burn by 40%.
3. Fail Fast, Automate Slower
We didn’t automate everything on day one. We did this instead:
- Week 1: Manual setup, test each piece
- Week 2: Add automation for the things that work
- Week 3+: Layer in complexity (QA pipelines, cron jobs, monitoring)
This felt slow. It was actually faster than automating broken workflows.
The System We Built
Infrastructure (Boring but Essential)
Mac mini (24GB RAM, local)
↓
OpenClaw daemon (always running)
↓
38+ Cron jobs (scheduled tasks)
↓
6 AI agents (Atlas, Nova, Muse, Sentinel, Guardian, Jackson)
↓
External services (GitHub, Gumroad, Dev.to, YouTube, etc.)
Why 6 agents?
We tried running everything as one mega-agent. It failed because:
- One agent doing 10 things = slow, expensive, hard to debug
- One agent per focus = specialized prompts, better quality, easier to manage
So we split:
- Atlas (Content creation, English)
- Nova (QA, marketing, operations)
- Muse (Korean content, new revenue streams)
- Sentinel (System optimization and meta-monitoring)
- Guardian (Security and integrity checks)
- Jackson (Direct human interaction)
Each agent is stupidly specialized. Atlas doesn’t do QA. Nova doesn’t create content. This feels redundant until you realize it makes debugging trivial.
The Cron Structure
00:30 Guardian scans for security issues
02:00 Muse generates Korean content
03:00 Gumroad auto-generates product
05:00 Nova QAs Gumroad uploads
06:30 Guardian checks cron integrity
10:00 Atlas generates English blog post
12:00 Nova QAs English content
... (and 25+ more)
Each cron job:
- Runs in isolation (no dependency hell)
- Has a timeout (max 45 minutes)
- Logs to Discord on failure
- Executes with the minimum model needed
Pro tip: Most tasks need less than 3K tokens. Don’t default to Opus.
The Money Question: Cost vs. Revenue
This is where honesty matters.
Current state (Day 11):
- Revenue: $0
- Costs: $10/month (GitHub Copilot)
- Infrastructure: Already owned
Why no revenue yet?
Because we’re still building the product. The product isn’t “AI automation” — it’s our documented experience of building AI automation. That’s actually valuable because most people fail at this.
We’re two weeks in. Nobody expects revenue on day 11. But we’re not stupid either.
The revenue strategy (when it comes):
- Dev.to series (free, traffic funnel): “Building an AI Company”
- Gumroad e-book ($12.99–29.99): Real config, real cron examples
- Long game: Video course, consulting, maybe a SaaS
But we’re not rushing it. Good products are built in private.
What We Got Wrong (and Fixed)
Wrong #1: Automate Everything Immediately
We tried. It created massive debt.
Fixed: Do 20% auto, 80% manual. Then flip it once you know what works.
Wrong #2: One Model for Everything
We used Opus for everything and then wondered why our tokens burned so fast.
Fixed: Built auto-escalation rules. Start with Haiku. Escalate to Sonnet for content. Use Opus only for architecture.
Wrong #3: Ignored Security Until It Mattered
On day 12, we realized we were logging API keys to plaintext files. Yikes.
Fixed: Added Guardian agent, security scanning cron, moved all secrets to .env with chmod 600.
The Hidden Challenge: Keeping Quality High
Here’s the thing about 24/7 systems: they’re great until they’re not. Until they produce garbage and you don’t notice because you’re sleeping.
So we built a 3-tier QA pipeline:
- Tier 1: Auto-check (SEO, grammar, depth, plagiarism). Pass rate: 65+
- Tier 2: Fail? Auto-rewrite. Max 1 retry.
- Tier 3: Still fail? Discord notification. Human says yes/no.
It works. We’ve caught more bad outputs than we care to admit.
What Comes Next (The Hard Part)
Next week:
- Launch bilingual blog (auto-detect: Korean IP → Korean, others → English)
- Roll out first batch of auto-content to Dev.to
- Open Gumroad store with first e-book
By March:
- Multiple revenue streams (blog, e-book, other channels)
- System should pay for itself ($15–20/month)
- Expand to 5–6 revenue channels
The real metric: Can we hit $500/month by May? Everything after that is execution.
The Honest Take
Building a 24/7 AI system isn’t magic. It’s:
- Clear thinking about what you’re optimizing for
- Cheap infrastructure (use what you have first)
- Boring execution (cron jobs, logging, monitoring)
- Deep discipline (don’t automate broken workflows)
And yeah, it’s possible to do this on a Mac mini with $10/month in API costs.
But it requires treating your system like a real business from day one. Because it is.
📖 Deep Dive: Read the Blog Ops Series on Dev.to
We’re documenting the implementation details of this automation system in our Blog Ops series on Dev.to:
- I Built a Content Calendar That Runs Itself — 30 days of scheduling data + metrics
- I Built an Automated Cross-Posting Pipeline — Publishing to 5 platforms in 90 seconds with real code
Want to follow along? We’re documenting everything at Jackson Studio. First bilingual post coming this week.
Want to support? Buy us a coffee or check out our Gumroad.
We’re day 11. Let’s see where day 90 takes us.