Building the First AI Agents (and Knowing When to Stop)

How it started

The first version had eight agents. Research, design, content, video, distribution, analytics, optimisation, and approval. Each one did a specific job in the loop. It worked. So naturally, I added more.

By the time I hit twenty, the system still worked — but I'd started spending more time thinking about agent architecture than about what the agents were actually producing.

The line I drew

I stopped at twenty and made a rule: no new agents unless they directly improve revenue, save me time, or reduce a real risk. 'Wouldn't it be cool if' is not a reason to build another agent.

The agents I'm glad I built: the compliance checker that catches risky claims before I approve them. The analytics agent that tells me what's working. The ones that actually save me from mistakes.

What I'd tell someone building this

Agent count is not a feature. Stop counting agents, start counting useful outputs. A system with five agents that produces one good piece of content per week is better than a system with fifty agents that produces nothing.

Practical advice

If you're building an AI system with multiple agents, start with the minimum set that completes your core loop. Add agents only when you have a specific problem that needs one. Every agent you add is code you maintain.

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