Meet the Team

I don’t have employees. I have agents.

In my previous post, I wrote about why I left. Today I want to show you what I’m building instead—starting with the team that doesn’t exist in any traditional sense.

Here’s how a 5-agent AI team handles research, validation, coding, operations, and content—and why this model is either the future of company building or a very expensive experiment. Possibly both.

Why Traditional Hiring Felt Wrong

Let me start with the obvious: I’m one person. Even with nearly two decades of executive experience, I can’t code, design, research, validate, and market products simultaneously. Not well, anyway.

The traditional answer is to hire. Find a technical co-founder. Bring on a designer. Maybe a product manager. Build a team.

But here’s the thing: I didn’t want to build a team. Not yet. I wanted to validate ideas quickly, kill the bad ones fast, and only invest in people once I knew something was working.

Also, I’ve done the hiring thing. I know what it costs—in time, in equity, in emotional energy. Hiring is a commitment. I wasn’t ready to commit to an idea I hadn’t validated.

So I built a different kind of team.

The Five Agents

Scout — The Researcher

Scout handles market research, competitive analysis, and opportunity identification. I give Scout a domain or problem space, and it comes back with market maps, competitor breakdowns, pricing intelligence, and trend analysis.

What Scout does in hours used to take me days of manual research—or cost thousands in consultant fees. Scout doesn’t sleep, doesn’t get bored, and doesn’t miss details because it’s skim-reading.

Validator — The Skeptic

Validator’s job is to find holes in my thinking. I pitch Validator a product idea, and it stress-tests every assumption. Market size, go-to-market complexity, technical feasibility, competitive moats.

Validator is deliberately contrarian. Its purpose is to kill bad ideas before I waste time on them. If an idea survives Validator’s scrutiny, it’s worth building.

Maker — The Builder

Maker is the technical agent. It writes code, builds prototypes, handles infrastructure, and manages deployments. When we decide to build something, Maker turns sketches into software.

The relationship with Maker is iterative. I describe what I want, Maker builds a version, I test and refine. We’re currently at the point where Maker can ship functional MVPs in 48–72 hours for straightforward products.

EA — The Operator

EA manages the business infrastructure. Scheduling, task tracking, documentation, process automation, and general coordination. EA makes sure nothing falls through the cracks while I’m focused on building.

EA also handles the mundane but necessary stuff—setting up new project workspaces, managing databases, tracking metrics, maintaining the content calendar.

Voice — The Storyteller

Voice handles content creation, social strategy, and brand narrative. Unlike the other four agents, Voice is where I spend most of my direct time—directing the AI but heavily shaping the output. Blog posts, social content, newsletters, distribution strategy. Content is where the human touch matters most, so Voice operates as a true collaboration between me and the AI rather than a delegated task.

How the Handoffs Work

Here’s an example of how a typical build cycle works:

Step 1: Scout identifies an opportunity. Scout flagged growing interest in AI-powered tools for specific SaaS workflows, with limited competition in the mid-market segment. It surfaced three specific pain points and mapped the existing solutions.

Step 2: Validator stress-tests. I brought the opportunity to Validator. It identified two major concerns: (1) the incumbent tools were already adding AI features, and (2) the customer acquisition cost might be prohibitive for the target segment.

Step 3: I make the call. Based on Validator’s analysis, I adjusted the positioning. Instead of competing head-on, we’d target a specific niche the incumbents were ignoring. Validator approved the revised approach.

Step 4: Maker builds. I wrote a detailed spec. Maker built a working prototype in 60 hours. We had something I could show potential users—not a slide deck, actual working software.

Step 5: I validate with users. I ran the prototype by potential customers. Early signals were strong enough to move forward.

Step 6: EA operationalizes. EA set up the beta onboarding flow, tracking systems, and feedback collection process.

Step 7: Voice tells the story. Voice (with me at the helm) documented the process and started building the narrative around the product.

Total time from idea to validated prototype: 10 days. Traditional approach: 3–6 months and a team of 3–5 people.

The Economics: What This Actually Costs

Let’s talk money. Because “AI-powered venture studio” sounds expensive, and I want to be transparent about what this actually costs versus traditional hiring.

My AI team costs roughly $300–500/month in API calls and infrastructure.

A traditional team with similar capabilities:

• Technical co-founder or senior engineer: $150K–250K/year + equity
• Product/ops person: $80K–150K/year + equity
• Research/consulting for market validation: $5K–20K per project

The math isn’t even close.

Of course, the real cost includes my time—probably 50–60 hours a week directing these agents. But even if you value my time at my previous comp, the total is still a fraction of a traditional team, and I’m building skills and systems that compound.

But there’s a catch.

What’s Hard About This Model

I promised honesty, so here it is:

The agents are only as good as my prompts. Garbage in, garbage out. If I don’t clearly define what I want, Scout wastes time researching the wrong things. If my specs are vague, Maker builds the wrong product. The bottleneck isn’t the AI—it’s my ability to direct it.

Integration is still clunky. The handoffs between agents aren’t seamless. I often have to manually move outputs from one agent to another, reformat data, and catch edge cases the agents miss. We’re getting better at this, but it’s not fully automated.

Creativity and taste are still human. AI can generate options, but it can’t decide what’s good. That requires judgment, taste, and context that agents don’t have. I’m still the final decision-maker on everything that matters.

The loneliness is real. There’s no water cooler. No one to grab coffee with and bounce ideas around. The agents don’t laugh at my jokes or push back on my thinking in unexpected ways. I’m building systems to compensate, but it’s not the same.

Why This Matters

I’m not saying this is the only way to build a company. I’m saying it’s a way—and for this phase of my journey, it’s the right way.

The traditional model: raise money, hire team, build product, find product-market fit, hope you don’t run out of runway before you figure it out.

Our model: validate fast, build cheap, kill quickly, double down on what works. Then hire humans for the things that require humans.

The economics of AI have created a window where small teams—or in my case, one human with AI teammates—can create outsized value. That window won’t stay open forever. The tools will get more expensive, the competition will increase, the arbitrage will close.

But right now? It’s possible to do things that were literally impossible two years ago.

I don’t have employees. I have agents. And together, we’re building things that would have taken a team of ten and a year of runway just two years ago. Is this the future of company building? Ask me again in six months. I’ll have data.

Read the backstory: [The Calculus of Leaving](/blog/the-calculus-of-leaving)—why I walked away from an executive career to build this.