Agents in the Wild: Where Research Meets Deployment· 野外的智能体:研究与部署的交汇
Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from research prototypes to production scale deployments across domains such as software engineering, scientific discovery, and finance. While academic work has emphasized benchmarks and algorithmic innovation, deployment raises new challenges around robustness, safety, and reliability. This tutorial brings together researchers and practitioners to explore advances in reasoning and planning, multi agent coordination, and evaluation, highlighting open challenges arising from deployment experience. Through applied case studies in pharmaceutical discovery and financial systems, we analyze common design patterns that make
探讨大型语言模型智能体研究与实际部署的挑战及进展
- 核心方法
- 通过药发现和金融系统的应用案例研究,分析多智能体协调、推理、规划的设计模式
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 解决智能体从研究原型到大规模生产部署过程中遇到的稳健性、安全性和可靠性问题
- 关键实验
- 未提供
- 主要贡献
- 提供智能体系统部署的实际经验,强调设计模式在解决部署挑战中的作用
- 意义与局限
- 对促进智能体系统在不同领域的安全可靠部署具有重要指导意义,但缺乏具体实验数据支持