📄 中文摘要
在生产环境中，数据和人工智能系统的设计需要从系统层面进行考虑，包括架构、代理和负责任的规模化。有效的系统架构能够确保数据流的高效性和可靠性，同时支持灵活的扩展。代理的设计则涉及如何智能地处理任务和决策，以适应不断变化的环境。负责任的规模化强调在扩展系统时，必须考虑伦理和社会影响，以确保技术的可持续发展。通过综合这些因素，可以构建出既高效又具备社会责任感的AI系统。
设计能够在生产中稳定运行的数据和人工智能系统
出处: Designing Data and AI Systems That Hold Up in Production
发布: 2026年2月26日
📄 English Summary
Designing Data and AI Systems That Hold Up in Production
Designing data and AI systems for production requires a system-level perspective that encompasses architecture, agents, and responsible scaling. Effective system architecture ensures efficient and reliable data flow while supporting flexible scalability. The design of agents involves intelligently handling tasks and making decisions to adapt to changing environments. Responsible scaling emphasizes the need to consider ethical and social impacts when expanding systems, ensuring sustainable technological development. By integrating these factors, it is possible to create AI systems that are both efficient and socially responsible.