The system collects data on restaurant operations and shares it via “Patty,” a voice that talks to employees through their headsets. If the drink machine is low on Diet Coke, Patty will tell the store’s manager. If a customer uses a QR code to report a messy bathroom, the manager will be alerted.
Anthropic与研究机构Material在2025年末对美国500多位技术领导者做了一次调研，近一半（46%）的组织认为与现有系统的整合是主要障碍。，推荐阅读服务器推荐获取更多信息
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The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.