How to Build a SaaS with AI Coding Agents (What Actually Works)

Every week there's a new "build a SaaS in 15 minutes" post. Someone strings together a no-code tool, an AI chatbot, and a Stripe checkout, and calls it a SaaS. It looks impressive in a demo video. It falls apart when real users show up.

My two most recent production SaaS products — MyOG.social and TheBlue.social — were built almost entirely with AI coding agents. After 30 years of shipping software and over a year of daily agentic coding, here's what actually works when you want to develop a SaaS application with AI.

What AI Coding Agents Actually Do Well

AI coding agents are excellent at:

Boilerplate and CRUD — routes, controllers, database queries, form validation. The stuff that's been written a hundred times, whether or not you've done it yourself. The agent writes it correctly and fast.
UI work — layouts, components, styling, responsive design. Especially with Tailwind. Describe what you want, iterate visually.
Tests — generating test cases, especially for pure functions and API endpoints. The agent catches edge cases you'd skip.
Following established patterns — if your codebase has a consistent style, the agent matches it. This is where a well-structured codebase and project configuration pay off enormously.
Refactoring — renaming, extracting functions, moving code between files. Mechanical work the agent does without mistakes.

Where They Fail

They're bad at:

Architecture decisions — the agent will happily build whatever you describe, even if it's the wrong abstraction. You still need to think about structure.
Database migrations — I run migration generation manually. Schema changes are one area where you want to check out the change before generating the database migrations (and running it).
Security-sensitive code — auth flows, permission checks, input sanitization. The agent can write it, but you need to verify it carefully.
Knowing when to stop — agents will keep adding code if you let them. Unnecessary abstractions, premature optimizations, over-engineered solutions. You have to be the one who says "that's enough."

The implication: your starting codebase matters more than which AI tool you use.

Your Starting Point Matters More Than Your AI Tool

People obsess over which AI coding agent to use. Cursor vs. Claude Code vs. Copilot vs. Codex. As long as you're using a frontier or near-frontier model, it matters less than you think.

What matters is the codebase the agent is working in. An agent operating on a well-structured project with clear conventions produces dramatically better code than the same agent working on a blank slate. The difference isn't subtle — it's the difference between production-ready output and code you'll rewrite in a week.

The single most effective thing I do for agent quality is maintaining an AGENTS.md file. It's a markdown file at the root of the project that the agent reads on startup. It contains the project's architecture, coding conventions, available commands, and hints for common tasks. Without it, the agent guesses. With it, the agent knows how to run tests, what patterns to follow, and what to avoid.

This is why I built Stacknaut — a production-grade starter kit with AI agent configuration baked in. It's extracted from the same codebase I use to run MyOG and TheBlue. When I start a new project, auth, billing, deployment, and the agent config are already wired up. I skip straight to building the product. The agent already knows the codebase because the AGENTS.md tells it everything it needs.

You don't have to use a starter kit. But you need something — a production-tested foundation with conventions the agent can follow. Starting from npm init with an AI agent is how you end up with a codebase that works in demos and breaks in production.

The Stack That Makes AI Effective

I use TypeScript end-to-end: Vue 3 and Tailwind on the frontend, Fastify on the backend, PostgreSQL with Drizzle as the ORM. One language across the entire stack.

Why this matters for AI-assisted development:

TypeScript's type checker catches agent mistakes. When the agent changes a function signature, the compiler tells you everywhere that broke. This is your safety net.
Agents know TypeScript extremely well. It's one of the most-represented languages in training data. The code quality you get from agents in TypeScript is noticeably better than in less common languages.
One language means one set of conventions. Your AGENTS.md doesn't need to explain different patterns for different languages. The agent context stays focused.

Most competing guides recommend React and Next.js. The framework matters less than people think. What matters is picking a stack where the agent can be productive, the type system catches mistakes, and you can deploy without a PhD in cloud infrastructure. Vue, React, Svelte — they all work. Pick one and go.

The boring choice is usually the right choice. Well-documented, well-supported tools that agents have seen in millions of codebases. Trendy tools generate trendy bugs.

The Real Workflow

Here's how I actually develop a SaaS application with AI coding agents on a typical day:

Interactive mode for the hard parts. Architecture decisions, complex business logic, anything security-sensitive. I write a detailed prompt, the agent generates code, I review the diff, iterate. This is where I spend my judgment.

Autonomous mode for the mechanical parts. I maintain a list of smaller TODOs — add a field here, fix a style there, write tests for this module. The agent picks them up, implements them, runs lint and type checking, and commits. I review the diffs later.

Skills that compose. I have reusable prompt files — skills — that chain together. "Take over" means: review the code, fix any issues, run the checks, commit, deploy. Two words and I walk away. These skills work across different agents because they're just markdown files.

A second model for code review. I use a different model to review what the first one wrote. A fresh perspective catches things the original author missed — same reason you'd have a second developer review a pull request.

Realistic timelines. With a production-ready starting point, I can ship a new feature in a day, a complete MVP in a week. Not 15 minutes. Anyone telling you otherwise is either selling something or hasn't deployed to production.

What No-Code and Vibe Coding Miss

(Side note: I find "vibe coding" dismissive of the real engineering work involved. I prefer "agentic coding" — you're still making decisions — including when to let the agent decide — just with a much faster executor. But the term is everywhere, so let's address it.)

No-code tools and "vibe coding" platforms are genuinely useful for prototyping. If you want to validate an idea in a weekend, they're great. But there's a gap between "it works in a demo" and "it runs in production," and that gap is where most SaaS products die.

What prototyping tools typically skip:

Real auth — not just a login form, but session management, token refresh, permission checks, rate limiting on auth endpoints.
Billing that handles edge cases — failed payments, subscription lifecycle, webhook idempotency, refunds. Stripe's happy path is easy. The other paths are where bugs hide.
Error handling — what happens when the database is down? When a third-party API times out? When a user sends malformed input? Production code handles these. Prototypes don't.
Deployment and infrastructure — your code needs to run somewhere you control, with SSL, monitoring, backups, and the ability to deploy a hotfix at 2am.
Ownership — if your SaaS runs on someone else's platform, you're one pricing change or terms update away from rebuilding everything. Your server, your code, no vendor lock-in.

The hidden cost of not understanding what you shipped is that you can't fix it when it breaks. And it will break.

From Built to Launched

Building the SaaS is half the work. The other half is everything between "it works on my machine" and "real users are paying for it." This is where most projects stall — not because the code isn't ready, but because the operational details feel overwhelming.

I've written a SaaS Launch Checklist that covers the full gap: infrastructure, auth, billing, SEO, monitoring, error handling, and the smoke tests to run before you flip the switch. After launch, the Post-Launch Hardening Checklist covers what to tighten up in the first few weeks.

If you're evaluating hosting costs, the SaaS Hosting Cost Calculator compares what you'd spend on Hetzner vs. Vercel, Render, and AWS. Spoiler: a single Hetzner server at EUR 5/month can run multiple SaaS products.

Ship It

The winning combination for building a SaaS with AI in 2026 is straightforward: a production-tested starting point, AI coding agents with good project configuration, and sound engineering practices. You don't need a team of ten. You don't need a complex microservices architecture. You don't need to spend six months on infrastructure.

You need a solid codebase, a well-configured agent, and the discipline to ship. The tools have never been better. The excuses have never been fewer.

Start building.