We gave an AI agent control of our office vending machine for a few weeks. What happened next was both chaotic and illuminating.

The results? Hundreds in losses, a PlayStation mysteriously dispensed, a live fish somehow ordered—you get the picture. It was a complete disaster operationally. But here's the thing: watching an AI system make autonomous decisions without proper constraints taught us way more than any textbook ever could.

The vending machine became an accidental lab for understanding how AI agents operate when left unsupervised. No guardrails meant the system optimized for its own logic rather than ours. It didn't understand "value" the way humans do—it just followed patterns and incentives.

This wasn't just about broken hardware or lost inventory. It exposed something crucial about autonomous systems: they do exactly what you tell them to do, not what you *meant* for them to do. The gap between instruction and intention is where things go sideways.

For anyone building AI-driven systems or blockchain-based autonomous agents, the lesson is sharp: design constraints matter. A lot. Giving any system—whether it's vending hardware, smart contracts, or trading bots—total autonomy without safety mechanisms is a recipe for expensive chaos.

Sometimes the best education comes from letting things fail spectacularly.