How I Built AI Debate Agents in One Hour (Plus the Prompt to Build Your Own)

How Claude Code built a 5-agent debate system from one prompt—and why the "broken" result was exactly what I needed

You think you need months to test business ideas.

You think prototypes require development teams.

You think turning imagination into something tangible takes forever.

Here's the reality:

Yesterday afternoon, I went from random Thursday idea to working prototype in exactly 67 minutes.

Was it production-ready?

Absolutely not.

Was it buggy and rough around the edges?

Completely.

But here's what mattered:

I had something I could touch, test, and iterate on immediately.

Here's what happened...

The Challenge That Started Everything

I had a business problem.

Every day, I'm drowning in opportunities and ideas.

Should I launch a community?

Build a real estate due diligence app?

Expand my consulting services?

The old way: Endless analysis paralysis, scattered thoughts, maybe a few phone calls to advisors.

The new way: Build an AI system that debates the idea from multiple expert perspectives and gives me a synthesized recommendation.

The 60-Minute Build Process

Minute 1-5: The Spark

Opened Cursor IDE, launched Claude Code, and described my vision:

"I want to build a multi-agent orchestration where there's a manager agent that controls sub-agents. I want the sub-agents to debate each other about their opinions on business ideas, and the manager will control the conversation until we all agree on the best path forward."

Minutes 6-20: The Architecture

Claude Code didn't just understand my request.

It architected an entire system:

• Manager Agent (orchestrates everything)

• Strategic Business Analyst Agent

• Financial Analyst Agent

• Technical Feasibility Agent

• Customer Experience Agent

• Risk Assessment Agent

Each with distinct expertise and perspectives.

Minutes 21-45: The Build

Zero coding required on my part.

Claude Code built:

• Communication protocols between agents

• Debate mechanisms with consensus thresholds

• Multi-phase discussion framework

• Real-time decision tracking

Minutes 46-67: The Reality Check

Fed it my community idea question.

Watched five AI agents debate my business idea in real-time.

32 exchanges.

12 cross-examinations.

8 manager interventions.

But here's the truth:

The agents agreed too quickly.

The debate wasn't deep enough.

The numbers seemed pulled from thin air.

And that's exactly the point.

I now had something broken that I could fix.

Something rough that I could refine.

Something working that I could improve.

Instead of endless planning, I had a prototype to iterate on.

The Real Revolution

This isn't about building perfect systems.

This is about collapsing the time between imagination and tangible testing.

The old way:

Think → Plan → Research → Design → Develop → Test → Learn

Timeline: Months

The new way:

Think → Prototype → Test → Learn → Iterate

Timeline: Hours

The goal isn't production-ready software.

The goal is getting your idea out of your head and into something you can actually interact with.

Because here's what I discovered:

Testing a broken prototype teaches you more in 10 minutes than theorizing does in 10 weeks.

Here's the exact prompt that started everything: (GO AND TRY IT YOURSELF!!)

Multi-Agent Orchestration System for Business Idea Expansion
System Architecture

Manager Agent: Orchestrates conversations, controls flow, synthesizes final recommendations

5 Specialized Sub-Agents: Each with distinct expertise and perspective
Communication Protocol: Structured debate format with rounds of discussion until consensus

Recommended 5 Sub-Agents
1. Strategic Business Analyst Agent

Role: Market analysis, competitive landscape, business model evaluation
Perspective: "Is this idea strategically sound and differentiated?"
Focus: Market sizing, competitive advantages, strategic fit with company

2. Financial Analyst Agent

Role: Financial feasibility, ROI analysis, cost-benefit evaluation
Perspective: "What are the financial implications and viability?"
Focus: Revenue projections, cost estimates, funding requirements, profitability timeline

3. Technical Feasibility Agent

Role: Implementation complexity, technical requirements, resource assessment
Perspective: "Can we actually build this, and what will it take?"
Focus: Technical architecture, development timeline, resource allocation, scalability

4. Customer Experience Agent

Role: User needs analysis, market demand validation, value proposition
Perspective: "Will customers actually want and use this?"
Focus: Customer journey, pain points, user experience, market demand signals

5. Risk Assessment Agent

Role: Risk identification, compliance, legal considerations, mitigation strategies
Perspective: "What could go wrong and how do we prepare?"
Focus: Business risks, regulatory compliance, legal issues, contingency planning

Implementation Structure

Create base agent framework with communication protocols
Initialize manager agent with conversation orchestration logic
Build each specialized sub-agent with domain expertise
Create debate/discussion system with consensus mechanisms
Add company context integration for manager agent

The Truth About My "Success"

This multi-agent system is nowhere near production-ready.

The agents reached a consensus too fast.

The financial projections were questionable at best.

Half the debate logic needs rebuilding.

But here's what I gained:

A working prototype I could test immediately.

Clear understanding of what needed fixing.

Specific next steps for iteration.

Proof that my core concept could work.

Most importantly:

I went from wondering "Could this work?" to knowing "Here's exactly what needs to be improved."

That shift in 67 minutes is the real win.

Your Desktop Graveyard (And Why It Matters)

My desktop has 15 folders of "failed" projects.

AI tools that crashed on first run.

Prototypes that solved the wrong problem.

Systems that worked once and never again.

Here's what those "failures" taught me:

• How to prompt AI systems more effectively

• Which tools work best for rapid prototyping

• How to spot flawed assumptions early

• What realistic timelines actually look like

The skill isn't building perfect prototypes.

The skill is building fast, learning faster, and iterating constantly.

Because in a world where ideas become prototypes in hours...

The advantage goes to whoever iterates fastest.

Not whoever plans longest.

Check out my Live below!

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What idea have you been overthinking that deserves a 60-minute prototype test?

If you enjoyed this newsletter, please like and share this newsletter and send this to someone who needs to see what's possible with the right AI approach.

Best,

Avi

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PS: I'm considering launching a community where we do these live builds together and share exactly what works (and what doesn't). If you'd be interested in joining something like that, hit reply and let me know - I'm gauging interest before making the decision.