Agent-E：从自主网络导航到智能体系统的基础设计原则
Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
AI总结：
本文介绍新型网络智能体Agent-E，通过分层架构、DOM蒸馏去噪和变化观察等改进，在WebVoyager基准上超越现有最先进智能体10-30%，并提炼出智能体系统设计的通用原则。
摘要：
人工智能智能体正在改变消费者和企业领域的工作方式。然而，构建高性能智能体或多智能体系统的设计模式和架构仍在发展中，对各种设计选择和算法的影响的理解也仍在不断深化。在本文中，我们介绍了构建新型网络智能体Agent-E的工作（我们的代码可在https://github.com/EmergenceAI/Agent-E获取）。Agent-E在先前最先进的网络智能体基础上引入了多项架构改进，如分层架构、灵活的DOM蒸馏和去噪方法，以及“变化观察”的概念，以引导智能体实现更准确的性能。我们首先展示了Agent-E在WebVoyager基准数据集上的评估结果，表明Agent-E在该基准的大多数类别中比其他最先进的文本和多模态网络智能体高出10-30%。然后，我们将从Agent-E开发中获得的经验综合为开发智能体系统的通用设计原则。这些原则包括使用领域特定的原始技能、环境观察的蒸馏和去噪的重要性、分层架构的优势，以及智能体自我改进在智能体积累经验时提升其效率和效能的作用。
英文摘要：
AI Agents are changing the way work gets done, both in consumer and enterprise domains. However, the design patterns and architectures to build highly capable agents or multi-agent systems are still developing, and the understanding of the implication of various design choices and algorithms is still evolving. In this paper, we present our work on building a novel web agent, Agent-E \footnote{Our code is available at \url{https://github.com/EmergenceAI/Agent-E}}. Agent-E introduces numerous architectural improvements over prior state-of-the-art web agents such as hierarchical architecture, flexible DOM distillation and denoising method, and the concept of \textit{change observation} to guide the agent towards more accurate performance. We first present the results of an evaluation of Agent-E on WebVoyager benchmark dataset and show that Agent-E beats other SOTA text and multi-modal web agents on this benchmark in most categories by 10-30\%. We then synthesize our learnings from the development of Agent-E into general design principles for developing agentic systems. These include the use of domain-specific primitive skills, the importance of distillation and de-noising of environmental observations, the advantages of a hierarchical architecture, and the role of agentic self-improvement to enhance agent efficiency and efficacy as the agent gathers experience.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
