Running an AI Agency With 151 Agents
What it actually looks like to run a full-service agency where the specialists are Markdown files and the humans are orchestrators.
The traditional agency model looks like this: a founder, a handful of account managers, a cast of specialists (designers, developers, strategists, writers), and a cadre of freelancers for overflow. Payroll is the biggest line item, and every new client stresses the hiring pipeline.
The emerging agency model looks different: two or three humans, 150 AI agents from msitarzewski/agency-agents, and a stack of skills and workflows that handle execution. The humans do client relationships, strategy, and quality control. The agents do everything else.
This article describes what that model actually looks like day to day, based on conversations with several founders already running it.
Key Takeaways
- Modern AI agencies can deliver full-service work with 2-3 humans and 150+ agents
- Human roles shift toward strategy, client relationships, and quality control
- Agent roles cover execution: design, content, code, analysis, reporting
- The model works best for SMB clients with defined scopes, less well for enterprise
- Margins improve dramatically: typical headcount cost drops 60-80%
The structure of an AI-first agency
A typical AI-first agency has three roles:
- Founder/Director. Handles sales, positioning, and final quality review. Speaks to clients.
- Orchestrator(s). Runs the agent workflows. Invokes specialists, composes outputs, and ships deliverables.
- Specialist humans (optional). For one or two high-leverage skills — maybe a senior copywriter or a branding director — where human judgment is genuinely irreplaceable.
That's it. No junior designer, no content intern, no project manager pool.
What the humans actually do
It would be tempting to say "the humans just push buttons," but that undersells their role. In practice, the human team does three things agents can't:
Client relationships. Clients hire agencies because they want a human to trust. Agents can write emails, but clients want to look another human in the eye on a Zoom call. This is the irreducible piece.
Strategy and judgment. Agents are great at executing a strategy, weak at choosing one. Deciding which direction to take a campaign, which hill to die on, which trade-off is worth making — those are human calls.
Quality control. Agent output is consistently good but occasionally wrong. A human final pass catches the 5% of cases where the agent misunderstood the brief or produced something off-brand.
What the agents actually do
Everything else. Here's a rough mapping for a typical marketing engagement:
- Discovery: UX Researcher agent drafts interview guides; humans run interviews
- Strategy: Brand Strategist and Marketing Strategist agents propose positioning options; human picks
- Content: Content Writer and Editor agents produce first drafts; human reviews and polishes
- Design: UI Designer agent writes specs; Frontend Developer agent implements; human reviews
- Analytics: Marketing Analytics Specialist agent sets up dashboards and reports
- Reporting: Marketing Data Analyst agent produces client-facing reports
For an engineering engagement, swap in agents from the 26 engineering agents roundup.
A day in the life
We spoke with three AI-first agency founders about their typical day. Here's a composite:
8:00 AM. Review client inbox. Reply to urgent messages. Forward project questions to the orchestrator.
9:00 AM. Client call. Discuss strategy for Q2 campaign. Take notes into a shared doc.
10:00 AM. Hand notes to the orchestrator. The orchestrator invokes the Brand Strategist and Content Strategist agents to produce options. Returns with three directions within 30 minutes.
11:00 AM. Review the three directions, pick one, send to client for sign-off.
12:00 PM. While waiting for client response, the orchestrator runs content production on last week's approved plan. Content Writer drafts 8 articles over the afternoon. Editor polishes. Delivery-ready by EOD.
2:00 PM. Client approves direction. Kick off production. Orchestrator chains UI Designer, Frontend Developer, and Copywriter.
4:00 PM. Quality control pass on morning's content deliverables. Small tweaks, then push to client.
5:00 PM. Send daily update email (drafted by the Cold Email Specialist agent, approved by human).
Three humans, one afternoon, output that would have required a team of eight in the old model.
Where the model breaks down
It's not all upside. The AI-first agency model has real limitations:
Complex custom software. For non-trivial engineering work, agents need heavy human supervision. The productivity gains are smaller.
Enterprise clients. Large enterprises want to see teams, headcounts, and org charts. The "two humans and 150 agents" pitch doesn't resonate even when the output is superior.
Highly regulated industries. Legal, medical, financial services all have approval workflows and liability concerns that make agent output risky without heavy review.
Brand-defining creative work. For a brand's flagship campaign, most clients still want a human creative director in the room.
Economics
The economics are what drive the trend. A traditional 10-person agency might have $1.2M in annual payroll. An AI-first equivalent has $250k in payroll and maybe $5k in API costs. That's $900k of margin that didn't exist in the old model.
Of course, this invites price compression. As more agencies adopt the model, prices will fall. First movers capture outsized margin for a window of time, then the game normalizes.
For a broader take, see The Case for Hiring 10 AI Agents Tomorrow.
The rise of orchestration
The critical skill for this model is orchestration. The orchestrator role didn't exist five years ago. Today, it's the highest-leverage seat at any AI-first agency.
Orchestrators understand the agent library, know which specialists to invoke for each problem, and compose outputs into coherent deliverables. They're part project manager, part prompt engineer, part quality controller. Read more about the dedicated Agents Orchestrator agent that helps human orchestrators scale.
Frequently Asked Questions
How do you bill clients?
Most AI-first agencies bill retainers or project fees, not hours. Hourly billing doesn't capture the value when your "hours" are actually minutes of machine time.
Do clients know you're using agents?
Transparency varies. Some agencies advertise it as a differentiator (faster, cheaper). Others keep it quiet and compete on output quality.
What stops a client from running the agents themselves?
Same thing that stops them from hiring a bunch of specialists directly: they don't know how, don't have the time, and don't want to. Agencies sell the orchestration.
How do you stay ahead as the model commoditizes?
Deeper domain expertise, better client relationships, and proprietary workflows that combine public agents with internal secret sauce.
Is this sustainable as more people catch on?
The window of outsized margins will close. What remains is a more efficient agency model with lower entry costs. Good for buyers, good for bootstrappers, hard for incumbents.
The future is orchestrated
The AI-first agency model isn't hype. It's already operating, already profitable, and already scaling. The only question is how fast it spreads.
Browse all 150 agents at aiskill.market/agents or submit your own skill.