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

2025-07-13

AI Summary

Traditional single-purpose AI systems exhibit significant limitations in task decomposition, cross-agent coordination, and experience reuse, rendering them inadequate for high-autonomy intelligence in complex, dynamic environments. To address this, we propose a collaborative, end-to-end multi-agent system framework. Our method introduces three core innovations: (1) an Agent-to-Agent communication layer leveraging the Model Context Protocol to enable asynchronous, context-aware, standardized interaction; (2) an Experience Pack memory architecture that unifies short- and long-term memory, supports verifiable experience retention, and facilitates continual learning; and (3) an embedded dynamic safety verification mechanism ensuring reliability during collaborative execution. Experimental evaluation on complex task orchestration benchmarks and real-world case studies demonstrates that our framework significantly outperforms state-of-the-art baselines in coordination efficiency, environmental adaptability, and experience reuse capability.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multi-Robot Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 extbf{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.

Problem

• Lack of coordination in traditional AI systems
• Insufficient memory reuse and task decomposition
• Limited scalability in dynamic enterprise environments

Innovation

• Protocol-driven multi-agent collaboration framework
• Standardized A2A communication with MCP
• Layered XP memory for knowledge retention