基于代理系统大语言模型(LLM)的架构能够推理,规划,行动,并与工具和其他代理协调，正在快速从研究原型过渡到跨领域的生产规模部署，例如软件工程,科学发现,和金融。虽然学术工作强调基准和算法创新, 部署，但围绕稳健性, 安全性, 和可靠性提出了新的挑战。本教程汇集了研究人员和从业者，探讨推理和规划, 多代理协调, 和评估, 方面的进展，强调部署经验带来的开放挑战。通过药物发现和金融系统, 中的应用案例研究，我们分析了使代理系统成功, 的常见设计模式，并讨论了故障模式, 的实用缓解策略，例如验证管道, 后备机制, 和人在环监督。与会者将获得对该领域的全面了解，以及具体的设计模式,评估清单,以及跨行业安全可靠部署的模板。
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 agentic systems successful, and discuss practical mitigation strategies for failure modes, such as verification pipelines, fallback mechanisms, and human in the loop supervision. Attendees will gain a comprehensive view of the field along with concrete design patterns, evaluation checklists, and templates for safe and reliable deployment across industries.
科目: 人工智能 (cs.AI); 计算和语言 (cs.CL)
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)