I was spending $200+ per month on one AI subscription doing everything myself.
Now I have 4 AI agents running my content, trading bots, and strategy for $100 per month total.
Here's exactly how I set it up.
Why One Agent Isn't Enough
Most people use AI like a single employee doing everything.
That's like hiring one person to be your writer, accountant, researcher, and receptionist.
You need specialists.
I learned this the hard way after watching my Claude Opus bill hit $247 in January. I was using one agent for tweet drafts, code reviews, email responses, trading analysis, and YouTube research.
It was like watching a brain surgeon also trying to answer phones and do bookkeeping.
The breaking point came when I asked my single agent to write tweets while also monitoring my trading bot. It kept context-switching between creative writing and risk management. The tweets topics it was researching got boring. The trading analysis got sloppy.
That's when I realized something. The best companies don't hire generalists for everything. They hire specialists.
So I built a team.
The 4 Agents I Built
🐺 Max (Chief of Staff)
Runs on Claude Opus. Oversees the whole operation. Manages the content pipeline, coordinates the other agents, builds and maintains the dashboard, monitors trading bots, and keeps detailed memory files between sessions.
Completed 47 tasks in 1 week.
Max is the one I actually talk to. He delegates work to the specialists, reviews what they produce, and handles anything that doesn't fit neatly into another agent's role. Built the entire Max HQ dashboard. Fixed my trading bot when it broke at 2 AM. Wired up the competitor research pipeline.
Think of him less like an employee and more like a COO who happens to also be a full-stack developer.
🌿 Sage (Content Writer)
Runs on Claude Haiku and Grok. Generates tweet ideas every 2 hours via cron jobs.
Produced 26 drafts in 1 week. ~40% approval rate.
Sage doesn't just write random tweets. He scrapes what's working for me. Studies my formats, hooks, and topics. Then generates drafts inspired by what's performing.
Every morning I wake up to fresh tweet ideas waiting in Max HQ for me to review.
The 40% approval rate might sound low. But that means I'm editing and giving it as much feedback as I can.
I went from spending hours on content to 20 minutes reviewing ideas and getting to the finish line faster.
🔐 Knox (Trading Ops)
Runs on Claude Sonnet and Opus. Monitors 3 Polymarket trading bots.
Runs health checks every 2 hours. Reports P&L, uptime, errors. Checks if they're online, and flags errors.
He caught that my Polymarket bot had a 92% win rate but was still losing money on uncertain bets. I would have kept letting that run if Knox wasn't obsessively tracking the numbers.
⭐ Nova (YouTube Strategy)
Runs on Claude Sonnet and Haiku. Daily research on trending topics, generates video ideas, tracks channel metrics.
Currently at 984 subscribers. Goal: 10K.
Nova runs once daily. Researches trending YouTube topics in crypto, AI, and personal branding, then generates video ideas with titles and hooks.
My top video was a Polymarket trading bot tutorial that hit 5,206 views. Nova's job is to find more angles like that. She scrapes my channel metrics and optimizes around what's actually performing, not what sounds cool.
The Model Tiering Trick
This is where the real savings come from.
Before: everything on one model = $200+ per month.
After: tiered approach = ~$100 per month.
Same output. Half the cost.
Here's the breakdown:
Haiku ($0.80/M tokens) for repetitive tasks:
Bot health checks (Knox runs 50+ times daily)
Trigger processing
Analytics scraping
Auto-claiming rewards
Sonnet ($3/M tokens) for 80% of real work:
Tweet drafts
Research summaries
Email responses
Conversation handling
Video ideas
Opus ($5/M tokens) for 10% heavy lifting:
Code architecture
Complex debugging
Multi-step reasoning
Strategic planning
The key insight: most AI work doesn't need the most expensive model.
Checking if a bot is online? That's a $0.80 Haiku job.
Writing a nuanced tweet about market psychology? That needs $3 Sonnet thinking.
Architecting a new trading system? Break out the $5 Opus.
Match the tool to the task. Don't use a Ferrari to deliver pizza.
How They Talk to Each Other
The agents aren't isolated. They share data through a central system I call Max HQ.
Here's how it works:
Shared Data Files:
drafts.json (Sage's tweet drafts)
agent-state.json (what each agent is working on)
visitors.json (analytics from my websites)
memory files (persistent context between sessions)
Cron Jobs:
Sage: Every 2 hours, generate tweet ideas
Knox: Every 2 hours, check bot health
Nova: Daily at 8 AM, research YouTube trends
Analytics scraping: Every 4 hours
APIs and Triggers:
Max HQ dashboard lets me request specific tasks. "Hey Sage, come up with 3 tweet ideas about today's Polymarket drama." Sage reads the trigger, generates drafts, saves to drafts.json.
Memory Management:
Each agent writes to daily memory files. Max reads everyone's memory and maintains the big picture.
It's like having a group chat where everyone stays updated.
The Results (Real Numbers)
Here's what actually happened in the first week:
Max (Chief of Staff):
47 tasks completed
Built the entire Max HQ dashboard
Wired up competitor research, content pipeline, trading bot monitoring
Maintains persistent memory across every session
Sage (Content Writer):
26 tweet drafts generated
~40% approval rate
Competitor accounts scraped and analyzed for inspiration
Runs every 2 hours automatically via cron
Knox (Trading Ops):
36 bot health checks completed
Caught the Polymarket risk/reward problem (92% win rate)
Monitors both Polymarket bots around the clock
Nova (YouTube Strategy):
4 strategy briefs delivered
Video ideas generated daily based on trending topics
Tracks channel metrics (984 subs, top video: 5,206 views)
Analytics (Automated):
Clips website: 1,717 visitors, 6,840 page views tracked automatically
Data scraped every 4 hours without me touching anything
The Polymarket Reality Check:
This is my favorite example of why agent monitoring matters. My trading bot had a 92% win rate. Sounds amazing right?
Most of its losses were 100% preventable though.
Why? Because it was buying false flag positions. And this matters when 1 loss can wipe out 20 wins. I would have kept running that bot for weeks thinking everything was fine.
Knox caught it because he's obsessively tracking the numbers every 2 hours. That's the kind of insight you miss when you check manually once a week.
How to Build This Yourself
Here's the step-by-step playbook:
Step 1: Install OpenClaw
This is the orchestration platform I use. Sets up your main agent with SOUL.md (who you are) and AGENTS.md (how you work).
Other options exist (Auto-GPT, LangChain) but OpenClaw handles the cron jobs and memory management automatically.
Step 2: Define Your Agent Roles
Don't copy my 4 agents. Look at your actual workflow:
What takes 2+ hours daily?
What do you check obsessively?
What would you hire an intern to do?
What requires specialist knowledge?
Start with 2 agents, not 4. I made the mistake of building all 4 at once. It's chaos.
Step 3: Create Cron Jobs for Each Specialist
Each agent runs in isolation. No shared context unless you explicitly build it.
Sage's cron job:
Knox's cron job:
Step 4: Use Model Tiering
This is crucial. Assign models based on task complexity:
Repetitive checks: Haiku
Creative work: Sonnet
Complex reasoning: Opus
Set this in each agent's config. Don't let Opus handle simple status checks.
Step 5: Build Simple Coordination
Shared JSON files work fine. Don't overcomplicate with databases.
Step 6: Set Up the Feedback Loop
Track what works. Adjust agent prompts based on results.
Sage was generating too many generic tweets. I updated his prompt to include "study my latest viral tweets before generating tweet ideas."
Approval rate went from 25% to 40%.
What I'd Do Differently
Honest lessons learned:
Start with 2 agents, not 4.
I built Max and Sage first. They worked great. Then I got excited and added Knox and Nova too quickly.
Spent 3 days debugging coordination issues that wouldn't exist with 2 agents.
Win rate is a vanity metric.
Knox's trading monitoring taught me this. 92% wins mean nothing if risk/reward is terrible.
Focus on net P&L, not win percentage.
Volume doesn't equal quality.
Sage was generating 6 drafts every 2 hours. Most were garbage.
I reduced it to 3 drafts with better prompts. Approval rate doubled.
Memory management is crucial.
Agents forget everything between sessions. Build persistent memory from day 1.
I lost weeks of good prompts because I didn't save them properly.
Model costs add up fast.
My first month bill was $250 because I used Opus for everything.
Haiku can handle 80% of tasks for 1/6 the cost.
The Bigger Picture
We're at the very beginning of AI agent teams.
Right now, most people use AI like it's 2010 and they just got their first smartphone. They're using it to do the same things they did before, just slightly faster.
Making phone calls instead of building apps.
But the real opportunity isn't replacing your current workflow. It's reimagining what's possible when you have 4 specialists working 24/7 for the cost of one Netflix subscription.
In 6 months, every serious operator will have multiple agents. The ones who figure out the orchestration patterns now will have a massive advantage.
Think about it. While your competitors are manually writing tweets and checking their bots, your team is running 24/7. Finding opportunities. Creating content. Monitoring risk.
You're sleeping. They're working.
The gap compounds daily.
I spent 1 week building this system. Now it saves me 3+ hours daily while producing better output.
That's 21 hours per week I can spend on strategy instead of execution.
My agents handle the routine stuff. I focus on the big picture.
That's the real insight here. It's not about replacing human intelligence. It's about freeing human intelligence to work on problems that actually matter.
Your $100 AI employee team is waiting.
The question isn't whether you'll build it.
The question is whether you'll build it before your competition does.
bookmark this. you'll thank me later.
Want to see the Max HQ dashboard and agent prompts? I'm documenting the entire build on my page @sharbel.
