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

TL;DR

GoalfyMax is a protocol-driven multi-agent system that enhances coordination, memory reuse, and task decomposition through standardized communication and layered memory architecture, enabling scalable and adaptable intelligent enterprise solutions.

Contribution

It introduces a novel protocol-driven framework with a standardized communication layer and layered memory system for improved multi-agent collaboration and continual learning.

Findings

Achieves superior adaptability and coordination in complex tasks

Demonstrates effective experience reuse and knowledge retention

Outperforms baseline frameworks on benchmark tasks

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.