GoalfyMax: A Protocol-Driven Multi-Agent System for Intelligent Experience Entities

• URL: http://arxiv.org/abs/2507.09497v1
• Date: Sun, 13 Jul 2025 05:13:52 GMT
• Title: GoalfyMax: A Protocol-Driven Multi-Agent System for Intelligent Experience Entities
• Authors: Siyi Wu, Zeyu Wang, Xinyuan Song, Zhengpeng Zhou, Lifan Sun, Tianyu Shi,
• Abstract summary: We present GoalfyMax, a protocol-driven framework for end-to-end multi-agent collaboration.<n>GoalfyMax introduces a standardized Agent-to-Agent (A2A) communication layer built on the Model Context Protocol (MCP)<n>It incorporates the Experience Pack (XP) architecture, a layered memory system that preserves both task rationales and execution traces.
• Score: 4.406205045227101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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