[论文] Agents in the Wild: Where Research Meets Deployment

论文概要

研究领域: NLP
作者: Grace Hui Yang, Pranav N. Venkit, Hooman Sedghamiz
发布时间: 2026-07-22
arXiv: 2507.17082

中文摘要

基于大语言模型（LLM）的智能体架构，具备推理、规划、行动以及与工具和其他智能体协调的能力，正迅速从研究原型过渡到软件工程、科学发现和金融等领域的生产级部署。虽然学术工作强调基准测试和算法创新，但部署带来了关于鲁棒性、安全性和可靠性的新挑战。本教程汇集研究者和实践者，探讨推理与规划、多智能体协调和评估的进展，突出部署经验中出现的开放挑战。通过药物发现和金融系统的应用案例研究，我们分析使智能体系统成功的常见设计模式，并讨论故障模式的实际缓解策略，如验证流程、回退机制和人在回路监督。与会者将获得该领域的全面视角，以及跨行业安全可靠部署的具体设计模式、评估清单和模板。

原文摘要

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原型 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 s...

自动采集于 2026-07-23

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