After 30 years of coding, I cracked AI agent teams by treating them like junior developers – clear jobs, specific prompts, and one supervisor to keep everything clean.
The story
What I just learned about building AI agent teams changed everything for me.
After 30 years of coding and 8 years with AI, I finally cracked it – building teams of AI agents isn’t about collecting fancy tools. It’s about creating a workflow where each agent has one job and does it well.
The breakthrough came when I stopped thinking like a tech enthusiast and started thinking like a team manager.
Break everything down first. Before I assign any agent a task, I chunk the problem into distinct pieces. No overlap. No confusion. If an agent’s job feels fuzzy, I split it further.
Prompts are everything. I write instructions like I’m briefing a junior developer – specific, direct, with examples. If I want a certain tone or style, I say it explicitly. Vague prompts get vague results.
Context is king. I only feed each agent what it needs. Nothing extra. I use variables to pass exactly the right data between agents, treating each handoff like a relay race.
Appoint a supervisor. When multiple agents are working, I assign one to manage the final output. Keeps things clean and consistent.
Test relentlessly. I change one thing at a time, check the logs, and run edge cases. I invite others to break my system because that’s where I learn the most.
Treat agents like code. Version control, testing, CI/CD – the whole deal. And for anything high-stakes, a human reviews before it ships.
This approach turned chaotic automation into actual collaboration. The agents don’t just work for me – they work with me.
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