迈向基于MCP服务器生态系统的声明性代理层
Towards a Declarative Agentic Layer for Intelligent Agents in MCP-Based Server Ecosystems
AI总结：
本文提出了一种声明性代理层，旨在解决基于MCP服务器生态系统中代理工作流的可靠性问题，通过明确的架构结构提升任务执行的可验证性和可重复性。
AI中文摘要：
近年来，大语言模型（LLMs）的进展使得开发越来越复杂的代理和多代理系统成为可能，这些系统能够进行规划、工具使用和任务分解。然而，实证证据表明，许多这些系统存在根本性的可靠性问题，包括幻觉行为、不可执行的计划和脆弱的协调。关键的是，这些失败并非来自底层模型本身的限制，而是由于缺乏将目标、能力和执行联系起来的显式架构结构。本文提出了一种声明性的、模型无关的架构层，用于基于MCP的服务器生态系统中的 grounded 代理工作流，以解决这一缺口。所提出的层称为DALIA（智能代理的声明性代理层），它正式化了可执行能力，通过声明性发现协议暴露任务，维护一个联合目录，记录代理及其执行资源，并构建仅基于声明操作的确定性任务图。通过强制在发现、规划和执行之间进行清晰的分离，该架构将代理行为限制在可验证的操作空间内，减少了对推测推理和自由形式协调的依赖。我们提出了该层的架构和设计原则，并通过一个代表性的任务导向场景来说明其操作，展示了声明性接地如何在异构环境中实现可重复和可验证的代理工作流。
英文摘要：
Recent advances in Large Language Models (LLMs) have enabled the development of increasingly complex agentic and multi-agent systems capable of planning, tool use and task decomposition. However, empirical evidence shows that many of these systems suffer from fundamental reliability issues, including hallucinated actions, unexecutable plans and brittle coordination. Crucially, these failures do not stem from limitations of the underlying models themselves, but from the absence of explicit architectural structure linking goals, capabilities and execution. This paper presents a declarative, model-independent architectural layer for grounded agentic workflows that addresses this gap. The proposed layer, referred to as DALIA (Declarative Agentic Layer for Intelligent Agents), formalises executable capabilities, exposes tasks through a declarative discovery protocol, maintains a federated directory of agents and their execution resources, and constructs deterministic task graphs grounded exclusively in declared operations. By enforcing a clear separation between discovery, planning and execution, the architecture constrains agent behaviour to a verifiable operational space, reducing reliance on speculative reasoning and free-form coordination. We present the architecture and design principles of the proposed layer and illustrate its operation through a representative task-oriented scenario, demonstrating how declarative grounding enables reproducible and verifiable agentic workflows across heterogeneous environments.
