The Rule Trap: Why AI Agents Demand Capability-Based Architecture

Hardcoded automation rules create a scaling problem: they anticipate only known scenarios, while AI agents constantly discover situations that have no pre-written response. The architectural flaw is treating AI as a rule-generator rather than a decision-maker—AI understands context, but translating that understanding into device commands still requires advance preparation. The solution is distributed capability-based architecture, where devices declare their abilities rather than waiting for centralized automations to trigger them.