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What happens when you give AI agents real roles, autonomy, and the ability to reflect on their own failures? In this video, I document an experiment using Claude Opus 4.6's agent teams feature to build a multi-user to-do list application—but the app itself isn't the point. This experiment explores whether AI agents can behave like a real software development team: organizing themselves, making decisions, failing, adapting, and improving. What emerged was a compressed version of the classic team lifecycle—forming, storming, norming, and performing. What You'll See: • How I assembled a cross-functional AI team modeled after real practitioners (Kent Beck, Marty Cagan, Carson Gross, Steve Krug, and others) • The "Day Zero" meeting where agents negotiated their own team agreements and values • The chaotic storming phase where everything fell apart (just like real teams) • A self-directed retrospective where agents diagnosed their own concurrency problems • The norming phase where they proposed and implemented their own process fixes • The performing phase where quality and discipline became consistent Key Insight: This isn't about AI writing code. It's about how process, structure, and values shape outcomes—even when the team is made of AI agents. When given strong influences, real autonomy, and the ability to reflect, agents don't just generate output—they develop emergent behavior. The resulting application worked, the code was thoughtfully designed, and the team improved itself without micromanagement. Next Episode: I'll give this team vague, stakeholder-style product requirements (no architecture, no implementation details) and see how well they can translate intent into working software. About Me: John Wilger, Principal Engineering Consultant at Artium AI 🔗 Links & Resources: https://artium.ai https://github.com/jwilger/tidyup-tango AI #SoftwareEngineering #ClaudeAI #AgentTeams #SoftwareDevelopment #MachineLearning #TDD #ProductManagement