Building Q-EOS: When Control Theory Meets Multi-Agent AI Governance
How I built a six-agent token economy governance system grounded in academic research — and what I learned about why architecture matters more than algorithms.
The Problem I Wanted to Solve
Token economies are fragile. When a stablecoin loses its peg, the typical response is a static rule: "if price drops below X, buy Y tokens." But static rules are pro-cyclical — they buy aggressively when the treasury is already stressed, and they ignore the difference between a temporary dip and a structural collapse.
I wanted to build something smarter. Not just "LLM makes decisions" — but a system where multiple specialized agents collaborate, check each other, and maintain safety guarantees even when individual components fail.
