GoalfyMax：面向智能体验实体的协议驱动多智能体系统
GoalfyMax: A Protocol-Driven Multi-Agent System for Intelligent Experience Entities
AI总结：
针对传统单用途AI系统协调、记忆复用和任务分解能力不足的问题，提出协议驱动的多智能体框架GoalfyMax，通过标准化通信层、分层记忆系统等功能提升适应性与协调性，实证结果优于基线框架。
摘要：
现代企业环境需要具备高度自主性和适应性、能够处理复杂、动态且多方面任务的智能系统。然而，传统单用途AI系统往往缺乏足够的协调、记忆复用和任务分解能力，限制了其在现实场景中的可扩展性。为应对这些挑战，我们提出GoalfyMax，一个协议驱动的端到端多智能体协作框架。GoalfyMax引入了基于模型上下文协议（MCP）的标准化智能体间（A2A）通信层，允许独立智能体通过异步、符合协议的交互进行协调。它整合了体验包（XP）架构——一种分层记忆系统，可保留任务理据和执行轨迹，实现结构化知识留存与持续学习。此外，我们的系统集成了多轮上下文对话、长短时记忆模块和动态安全验证等高级功能，支持稳健的实时策略适配。在复杂任务编排基准和案例研究上的实证结果表明，与基线框架相比，GoalfyMax在适应性、协调性和体验复用方面表现更优。这些发现凸显了其作为可扩展、面向未来的多智能体智能系统基础的潜力。
英文摘要：
Modern enterprise environments demand intelligent systems capable of handling complex, dynamic, and multi-faceted tasks with high levels of autonomy and adaptability. However, traditional single-purpose AI systems often lack sufficient coordination, memory reuse, and task decomposition capabilities, limiting their scalability in realistic settings. To address these challenges, we present \textbf{GoalfyMax}, a protocol-driven framework for end-to-end multi-agent collaboration. GoalfyMax introduces a standardized Agent-to-Agent (A2A) communication layer built on the Model Context Protocol (MCP), allowing independent agents to coordinate through asynchronous, protocol-compliant interactions. It incorporates the Experience Pack (XP) architecture, a layered memory system that preserves both task rationales and execution traces, enabling structured knowledge retention and continual learning. Moreover, our system integrates advanced features including multi-turn contextual dialogue, long-short term memory modules, and dynamic safety validation, supporting robust, real-time strategy adaptation. Empirical results on complex task orchestration benchmarks and case study demonstrate that GoalfyMax achieves superior adaptability, coordination, and experience reuse compared to baseline frameworks. These findings highlight its potential as a scalable, future-ready foundation for multi-agent intelligent systems.
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。
