这篇论文从实际部署出发，总结了LLM智能体在软件、金融等领域的成功模式和失败应对，比只看benchmark的研究更有实战价值。
本教程汇总了基于大语言模型（LLM）的智能体系统在软件工程、科学发现和金融等领域的生产级部署经验。内容涵盖推理与规划、多智能体协调和评估等研究进展，以及来自实际部署的挑战。通过制药发现和金融系统的应用案例，分析了成功设计模式和失败缓解策略，包括验证管线、回退机制和人在环路监督。
Agents in the Wild: Where Research Meets Deployment
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.