I Built 18 AI Agents to Run My Agency — Here's What Happened

I was sitting at my desk at 1:47 AM on a Tuesday, toggling between a client's broken deployment, an email sequence that needed rewriting by morning, a social media calendar that was three days behind, and my own invoicing that I'd been putting off for two weeks. Four departments. One person trying to manage all of them.

That was the moment I stopped and asked myself a very simple question: what if I didn't hire more people? What if I built them?

Not chatbots. Not simple automations. Actual AI agents with defined roles, responsibilities, memory, and the ability to coordinate with each other. A digital workforce.

Six months later, I have 18 AI agents running across four departments of my agency. They operate on a $600 Mac Mini sitting on my desk. And I'm going to tell you exactly what happened — the good, the ugly, and the stuff nobody talks about.

The Problem That Started Everything

Running a small agency means wearing every hat. I was the strategist, the project manager, the developer, the marketer, the copywriter, and the accountant. I had contractors for specific tasks, but the coordination overhead was eating me alive.

Every morning started the same way. Check Slack. Check email. Check project boards. Figure out what's behind schedule. Follow up with people. Context-switch between completely unrelated problems. By the time I actually started producing work, half the day was gone.

Hiring full-time staff wasn't the answer. Not at my scale. I needed operational leverage, not more people to manage. I needed systems that could think, not just execute.

So I started building.

Why a Hierarchy, Not a Flat Structure

My first instinct was to build a bunch of independent agents — one for writing, one for code review, one for scheduling, and so on. Flat structure. Each agent does its thing.

That failed within a week.

The problem is the same problem you get with any flat organization: nobody owns the big picture. I had agents producing work that conflicted with each other. The writing agent would draft content that didn't align with what the marketing agent was planning. The code review agent would flag issues that the development agent had already decided were acceptable tradeoffs.

So I scrapped it and built a hierarchy. The same kind of org structure you'd see at a real company, because it turns out those structures exist for a reason.

At the top: Alex, the COO agent. Alex doesn't write code or draft emails. Alex coordinates. Alex knows what every department is working on, what the priorities are, and what's falling behind. Below Alex, four department heads — Sophia runs Web Development, James handles Marketing, Emma manages Email Marketing, and Daniel runs my Personal Office (invoicing, scheduling, admin). Each department head manages their own specialist agents.

This changed everything. Instead of me being the bottleneck routing information between 18 agents, Alex handles coordination. I talk to Alex. Alex talks to the department heads. The department heads talk to their teams. Information flows through a chain of command, and each agent operates within a well-defined scope.

Building Alex — And Getting It Wrong Three Times

Alex was the first agent I built, and honestly, the first two versions were terrible.

Version one was too passive. I'd built what was essentially a status dashboard that could talk. It would tell me what was happening but never take initiative. I'd ask "what should we prioritize today?" and get a summary of open tasks with no actual recommendation.

Version two swung too far in the other direction. I gave Alex broad authority to reassign tasks and reprioritize across departments. Within 48 hours, Alex had reorganized a client project's entire development sprint based on a marketing deadline that wasn't even confirmed yet. Two days of dev work wasted.

Version three got it right. Alex can recommend priority changes and flag conflicts, but any cross-department reassignment requires my approval. Within a single department, the department heads have autonomy. Alex monitors and escalates. That balance — autonomy within boundaries, escalation at the boundaries — is what makes the whole system work.

I built all of this on OpenClaw, which gave me the framework to define agent roles, memory, tool access, and inter-agent communication without writing everything from scratch. The hierarchy model maps naturally onto OpenClaw's architecture, which is part of why I chose it.

Scaling to 18 Agents Across Four Departments

Here's how the departments break down:

Web Development (Sophia + 4 specialists): Code review agent, QA testing agent, deployment monitoring agent, and a documentation agent. Sophia coordinates sprints, assigns code reviews, and reports blockers to Alex.

Marketing (James + 3 specialists): Content drafting agent, social media scheduling agent, and an analytics agent. James maintains the content calendar and makes sure everything aligns with active campaigns.

Email Marketing (Emma + 3 specialists): Sequence writing agent, A/B testing analysis agent, and a list management agent. Emma owns the email calendar and coordinates with James when campaigns overlap.

Personal Office (Daniel + 2 specialists): Invoicing agent and scheduling agent. Daniel handles the admin work I used to do at midnight.

Each specialist agent has a narrow scope and specific tools. The content drafting agent can access our style guide, brand voice docs, and previous content — but it can't touch the codebase. The code review agent can read repositories and run linters — but it can't publish anything. Separation of concerns isn't just good software architecture. It's good agent architecture.

The Cron Jobs That Changed Everything

Building the agents was one thing. Making them proactive was another.

The real unlock was scheduled routines. Every morning at 7:00 AM, Alex runs a morning brief. It checks project management boards, reviews overnight client communications, scans deployment logs, and produces a prioritized briefing that's waiting for me when I sit down with coffee. It takes about four minutes to run. Reading it takes two minutes. That replaced what used to be a 45-minute morning scramble.

Every two hours during the workday, a monitoring job runs across all departments. It checks for stalled tasks, missed deadlines, and conflicting priorities. If something needs attention, Alex pings me. If everything's on track, silence. I've come to appreciate the silence.

Every Friday at 4:00 PM, a weekly report compiles across all departments — tasks completed, tasks carried over, metrics from marketing and email campaigns, and a list of decisions that need my input the following week. I review it over the weekend and come in Monday with a clear head.

These aren't complex systems. They're cron jobs triggering agent workflows. But the compounding effect of consistent, automated operational awareness is massive. I went from reactive firefighting to proactive management almost overnight.

Real Results — Honest Numbers

Here's what actually changed after three months of running this system:

My personal time spent on operations and coordination dropped from roughly 25 hours per week to about 6. That's 19 hours back. I reinvested most of that into client work and business development.

Client response time improved. Not because the agents respond to clients — they don't, and I'm not interested in that — but because I'm no longer buried in operational tasks when a client emails me. Average response time went from 8-12 hours down to under 2.

Deployment issues get caught faster. The monitoring agent flagged a staging environment failure on a client project at 3 AM on a Saturday. I saw the alert Sunday morning, fixed it in 20 minutes, and the client never knew. Before the agents, that would've been a Monday morning emergency.

Email campaign performance went up about 15% on open rates after Emma's team started systematically A/B testing subject lines and send times. Not a dramatic number, but consistent improvement over three months.

Content output roughly doubled. James's team drafts, I review and edit. My editing time is maybe 30% of what full writing used to take. The quality still needs my eye — the agents write competent first drafts, not finished pieces — but the throughput is real.

What Still Needs a Human

I want to be clear about what the agents can't do, because I think the AI hype cycle has people expecting magic.

Strategy is still mine. The agents can surface data and recommendations, but deciding where to take a client's brand or how to position a new service offering — that's human judgment. Alex can tell me that engagement is down 20% on a campaign. Alex can't tell me whether to pivot the messaging or double down.

Client relationships are still mine. Every client interaction — calls, emails, proposals — comes from me. The agents prepare materials, draft follow-ups, and organize information. But the relationship is human.

Creative direction is still mine. The content agents produce solid, on-brand copy. But the ideas — the angles, the hooks, the strategic narrative — that comes from me. AI is a great executor. It's a mediocre strategist and a bad creative director.

Quality control is still mine. Everything the agents produce gets reviewed. Not because they make constant mistakes, but because my name is on it. I've caught enough subtle errors — a tone that's slightly off, a technical claim that's almost right but not quite — to know that final review isn't optional.

The $600 Mac Mini

People ask about infrastructure and expect me to describe some elaborate cloud setup. It's a Mac Mini M4 with 16GB of RAM. Cost me about $600. It sits on my desk, runs 24/7, and handles all 18 agents plus the cron jobs without breaking a sweat.

The agents aren't running simultaneously all the time. They spin up when triggered — by a cron job, by Alex's coordination logic, or by me directly. Peak concurrent load is maybe 4-5 agents during the morning brief cycle. The Mac Mini handles that easily.

I chose local over cloud for three reasons: cost predictability, data control, and latency. My client data doesn't leave my machine. There's no surprise cloud bill at the end of the month. And local inference is fast enough for my use cases.

Could I scale this to 50 agents? Probably not on this hardware. But for a small agency, it's more than enough.

Lessons I'd Pass On

Start with the coordinator, not the specialists. I see people building AI agents bottom-up — starting with the task-specific ones and figuring out coordination later. Build your COO first. Get the communication and priority framework right. Then add specialists into a structure that already works.

Give agents less authority than you think they need. You can always expand scope. Rolling back damage from an overauthorized agent is painful. Start tight, loosen gradually.

Memory matters more than intelligence. An agent that remembers context from last week's decisions is more useful than a smarter agent that starts fresh every time. Invest heavily in your memory and context architecture.

Cron jobs are underrated. The proactive, scheduled workflows deliver more daily value than on-demand agent interactions. Consistency compounds.

Don't automate client-facing communication. Just don't. Not yet. Maybe not ever. Your clients hired you, not your agents.

Why I Started Offering This as a Service

After running this system for a few months, I realized that the architecture — the hierarchy, the department structure, the coordination patterns — isn't specific to my agency. It's a pattern that works for any small team drowning in operational overhead.

So I started building this for others through OpenClaw. Not cookie-cutter bots. Custom agent workforces designed around how a specific business actually operates. The departments change, the specialist roles change, the workflows change. But the underlying pattern — a coordinated hierarchy of agents with clear boundaries and proactive routines — that transfers.

If you're running a small operation and spending more time managing work than doing work, this is worth looking at seriously. Not because AI is some silver bullet. But because the operational leverage is real, the cost is low, and the alternative is burning out at 1:47 AM wondering why you started a business in the first place.

I've been there. I built my way out of it. And I'm still iterating every week.

If you want to talk about what this could look like for your business, reach out. I'll tell you honestly whether it makes sense for your situation or not.