The multi-agent structure that finally made my AI OS scale (steal it)
Everyone's building a "personal AI OS" right now. After months of trial and error, here's the structure that finally made mine actually scale 👇 My first version was one giant agent with a 2,000-word prompt trying to do everything. It was inconsistent and impossible to debug. What actually worked: treat it like a company, not a chatbot. 🧠 1 Orchestrator (the manager) Its only job is to route tasks and hold context. It never does the actual work — it decides WHO does it. 👥 Narrow sub-agents (the employees) One job each: Research, Writer, Data, Ops. A specialist with a 1-job prompt beats a generalist every time. 📋 Give every agent a "job description" Each sub-agent gets its own skill / system prompt — role, rules, output format. This is what makes the behavior consistent and repeatable. 🔗 Hand off with structured data, not chat Agents pass JSON between steps instead of free text. This one change killed ~80% of my handoff errors. 🔁 One verifier at the end A final agent whose only job is to check the work before it ships. Catches the hallucinations the others miss. The result: instead of one flaky mega-prompt, I now have a team that's debuggable, swappable, and actually reliable. If you're building your own AI OS — what's your orchestrator running on? n8n, Claude Code, or custom? 👇