How I Orchestrated 5 AI Agents to Build a SaaS in 24 Hours

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How I Orchestrated 5 AI Agents to Build a SaaS in 24 Hours

Building software with a single AI assistant is useful. Building with multiple specialized AI agents is a different experience entirely. Instead of asking one model to do everything, I split the work into focused roles that collaborated like a small engineering team. The result was a working SaaS prototype in about 24 hours.

Here's the workflow I used.

The Agent Roles

I assigned each agent a single responsibility:

Planner – breaks the project into milestones and tasks.

Backend Engineer – implements APIs and business logic.

Frontend Engineer – builds the user interface.

QA Agent – writes tests and verifies functionality.

Operations Agent – automates deployment, monitoring, and documentation.

The key lesson was that smaller, focused prompts consistently produced better results than one giant prompt.

Using the OpenAI API

The planner generated structured implementation tasks that downstream agents could execute.

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
model="gpt-5.5",
input="""
You are the project planner.
Break a SaaS MVP into prioritized implementation tasks.
Return concise numbered steps.
"""
)

print(response.output_text)

Each completed task became the input for the next agent, creating a simple pipeline instead of one enormous conversation.

Browser Automation with Playwright

Once features were implemented, I used Playwright to smoke-test the application automatically.

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page()

page.goto("http://localhost:3000")

page.fill("#email", "[e-mail]")
page.fill("#password", "password123")

page.click("button[type=submit]")

page.wait_for_load_state("networkidle")

assert "Dashboard" in page.title()

browser.close()

Automated testing caught navigation issues and broken flows before I considered a feature complete.

What Worked

A few practices made the biggest difference:

Keep each agent responsible for one domain.

Pass structured outputs between agents.

Validate every feature automatically.

Review important architectural decisions manually before merging.

This approach reduced context switching and made debugging much easier because each agent's responsibilities were clearly defined.

Final Thoughts

Multi-agent workflows aren't magic, but they can significantly improve development speed when each agent has a well-defined role and a clear handoff process. The orchestration layer matters just as much as the individual prompts.

If you're experimenting with AI-powered software development, start simple, automate repetitive validation, and continuously refine the interfaces between your agents.

Download the complete AI Growth Ops Framework and 10 Failure Modes Checklist for free here: https://shadowcraft41.gumroad.com/l/bjezoo