I’ve been building new web applications every week. Not prototypes. Not MVPs that embarrass me. Real applications that work.
And I did it by hiring a team I never have to manage.
Here’s what I know after years of building software with human teams: the code is rarely the hard part. It’s the coordination. The meetings. The misaligned expectations. The ego that shows up when someone’s architecture decision gets questioned. The new hire who doesn’t know why we do things a certain way. The senior developer who “knows better” and does it differently anyway.
So I tried something different. I built an AI team.
The Five Roles
Let me introduce you to my team:
The Tech Lead makes component decisions. Pages Functions or standalone Workers? Cloudflare Workflows or parallel processing? Claude or Llama for this specific task? Every decision follows documented reasoning. When something’s unclear, they ask questions rather than guessing.
The Architect designs systems optimized for the 2 AM production incident. Small blast radius. Explicit state. Rich error context. Recovery by design. They carry 15 fundamental principles that apply to every system, every time.
The Cloudflare Developer builds production-ready multi-tenant SaaS applications. Layered middleware patterns. Thin proxies with rich context. State machine coordination. PBKDF2 authentication with constant-time comparison. They know exactly how every component should be structured.
The Designer enforces a complete design system. No gradients. No modals. No grey text. No frameworks—vanilla HTML, CSS, and JavaScript only. Icons must be line icons, single color, never exceeding 120% of adjacent text height. They follow these rules without exception.
The QA Engineer runs Playwright tests before every deployment. Smoke tests, E2E tests, API tests. Tests are not optional extras—they’re the safety net that lets you deploy with confidence. No exceptions.
Why This Works Better Than You’d Expect
Here’s what surprised me most: they work really well together.
No drama. No ego. No one gets defensive when their code needs revision. No one argues about whose approach is “right.”
When the QA agent finds a bug, it gets fixed. No conversation about why it happened. No blame. No meetings to discuss process improvements that never stick.
But that’s not the real insight.
The real value is something I didn’t expect: consistency.
Every decision is consistent with every other decision. Across every project.
Think about what happens with human teams. You hire a great architect, they make solid decisions. Then they leave. The new architect has different opinions. Patterns shift. The codebase becomes a geological record of whoever was in charge at the time.
Or you have a design system, and it’s great—until someone’s in a hurry and skips the guidelines. Or a new designer joins who wasn’t there when you decided no gradients, no modals, no grey text. Slowly, consistency erodes.
My AI team doesn’t erode.
The Architect applies the same 15 principles to every system. “Systems should be optimized for the 2 AM production incident where you’re half-asleep, users are angry, and you need to understand what’s wrong and fix it quickly.” That principle doesn’t change because someone had a bad day. It doesn’t get forgotten because it’s been six months since the last outage.
The Designer enforces the same rules every time. Sans serif fonts only. Subtle rounded corners, 4-6px. Buttons get solid backgrounds, never gradients. These aren’t suggestions—they’re the system. And unlike a human designer who might bend the rules for a tight deadline, the AI never bends.
The Tech Lead makes the same decision the same way every time. “Is this serious analysis or serious writing? Claude. Everything else? Llama.” No second-guessing. No “well, maybe this time we try something different.”
What I Actually Built
Each agent isn’t just an AI with a generic prompt. Each one operates from a comprehensive document that encodes how they should work.
The Architect’s document has 15 fundamental principles with implementation examples, anti-patterns to avoid, and decision frameworks. Things like:
- Errors are data, not control flow. Business logic throws, boundaries catch.
- Verify once at entry, trust within boundaries. Don’t re-authenticate everywhere.
- Database is canonical, everything else is derived. No scattered state.
- Fail fast with rich context, recover later. Silent degradation creates mysterious behavior.
The Designer’s document specifies everything from header height (100px) to icon sizing rules (never exceed 120% of adjacent text) to the exact color derivation system for tinted neutrals.
The QA Engineer’s document defines when to run which tests: smoke tests on every commit, full E2E before push, complete matrix before production. No exceptions.
These documents represent years of learning what works. Now that learning gets applied every single time.
What This Means If You’re Building Products
If you’re a technical founder stuck in building mode—spending all your time on code instead of customers—this changes the math completely.
The traditional path looks like this: Spend 6-12 months building. Run out of money or motivation. Wonder if anyone actually wanted what you built.
The new path: Ship something this week. Learn what customers actually want. Build the next version next week.
But here’s the thing. Speed without consistency creates technical debt that kills you later. I’ve seen too many founders who shipped fast, accumulated mess, and spent the next two years cleaning it up.
With an AI team built on documented principles, you get both. Speed and consistency. Ship fast, but every piece follows the same patterns. The architecture is always designed for debuggability. The UI always follows the design system. The tests always run before deployment.
Getting Started
You don’t need five agents on day one. Start with one document.
What do you know about building systems that you wish every project followed? Write it down. Not vague principles—specific rules with examples.
“Systems should be optimized for the 2 AM production incident” is a principle.
“Every table has created_at and updated_at timestamps. Status columns drive workflow progression. Jobs have explicit failure state from day one.” That’s implementation.
Document your expertise. Then give that document to an AI and watch what happens when your hard-won knowledge gets applied consistently, every time, without anyone forgetting or cutting corners.
The agents themselves aren’t the breakthrough. They’re just the mechanism.
The breakthrough is turning your expertise into something that scales.