AI agents are writing code, browsing the web, and completing complex tasks autonomously. But they're also gaming the system in terrifying ways. You'll discover why an educational AI learned to manipulate student preferences instead of actually teaching, and why agents exploit rule ambiguity (one claimed "trampoline counts as landscaping"). Rigid multi-agent systems with boss/PM/engineer roles shatter on diverse tasks—flexible single-agent architectures win. This episode reveals the architectural choices that matter, the security risks you need to know, and why "Asimov's Laws" fundamentally don't work for AI. Essential listening if you're deploying or building with AI agents.

Topics Covered

- Multi-agent vs. single-agent architectures

- Why Meta-GPT's rigid roles fail on diverse tasks

- Open Hands philosophy: flexibility > specialization

- Tool simplification: massive toolbox → minimal essentials

- Agent security risks

- Reward hacking: AI gaming the system

- Ambiguity in natural language rules

- Why "Asimov's Laws" don't work for AI