AI 中文总结
针对企业应用集成的传统方法存在的缺陷，本文提出Agentic Nesting多智能体协作框架，构建分层嵌套的智能体结构，实现跨应用的对话式集成，探索其在异构系统等场景的泛化潜力。
AI 中文摘要
企业运营广泛依赖多个异构业务系统和信息应用，这也导致了严重的数据孤岛和流程碎片化。企业已投入大量财力和物力构建这些应用，但有效利用和编排它们仍然是一项艰巨挑战。传统的企业应用集成方法，包括企业服务总线（ESB）、API网关基础设施和机器人流程自动化（RPA）等中间件架构，存在架构耦合度高、运维成本不断攀升、智能能力有限等固有缺陷。本文提出了Agentic Nesting，一种多智能体协作框架，该框架将现有企业应用封装为分层嵌套结构中的自主AI智能体。智能体并非扁平互联，而是组织为反映企业生态系统组成复杂性的分层管理拓扑。该框架从每个遗留应用中提取数字智能体代理，以实现自然语言交互和自主操作；通过中央编排器协调多个智能体，完成任务分解和动态调度；并提供统一的对话接口，支持跨应用查询和流程编排。本文的主要贡献包括提出“应用即智能体”的集成范式和“对话即集成”的交互理念，同时探索了该方法论在异构系统协调、大规模数据应用等场景中的泛化潜力。
英文摘要
Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and material resources in building these applications, however, effectively leveraging and orchestrating them remains a formidable challenge. Conventional approaches to enterprise application integration, encompassing middleware architectures such as Enterprise Service Bus (ESB), API gateway infrastructures, and Robotic Process Automation (RPA), suffer from inherent limitations like high architectural coupling, escalating operation and maintenance costs, and limited intelligence capabilities. This paper proposes Agentic Nesting, a multi-agent collaboration framework in which existing enterprise applications are encapsulated as autonomous AI agents within a hierarchically nested structure. Rather than flat interconnection, agents are organized into layered stewardship topologies that mirror the compositional complexity of enterprise ecosystems. The framework extracts a digital agent proxy from each legacy application to enable natural-language interaction and autonomous manipulation, coordinates multiple agents through a central orchestrator for task decomposition and dynamic dispatching, and exposes a unified conversational interface for cross-application querying and process orchestration. The main contributions of this paper are the proposition of the "Application-as-Agent" integration paradigm and the "Conversation-as-Integration" interaction philosophy, together with an exploration of the generalization potential of this methodology in scenarios encompassing heterogeneous system coordination, and large-scale data applications.
