Feb 24
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I designed every agent. I understand the architecture. And the output still surprised me — not because it was good, but because I can't trace how it got there. I built an ideation system using Claude Code's new agent teams feature — a free thinker, a grounder, an arbiter, and a handful of supporting agents that brainstorm, challenge each other, and converge on ideas. I gave them a real problem: rename an application that had outgrown its original purpose. They worked for 90 minutes and came back with something I would have paid a professional team to produce. This video isn't a tutorial on agent teams. It's about what happens when the output exceeds what the inputs should produce — and what that means for how we work with AI when we can't fully explain what it's doing. All source code for the ideation skill shown in this video is linked below. If you're working with Claude Code, building multi-agent systems, or exploring what AI can do beyond code generation, this is relevant. Whether you're just getting started with agent teams or you've been experimenting with multi-agent architectures, the real question here isn't how to set them up — it's what they're capable of when you give them room to work. This also touches on AI ideation, brainstorming with AI, prompt engineering for agent systems, and the broader question of trusting AI output you can't fully explain. https://github.com/bladnman/ideation_team_skill https://code.claude.com/docs/en/agent-teams #ClaudeCode #AgentTeams #AICoding #AIAgents #Anthropic 00:00 - Intro 02:11 - Agent Teams 02:38 - The Challenge 05:10 - First Results 08:34 - The Icon Challenge 10:38 - Conclusion 12:59 - Outtakes