The rapid evolution of Large Language Model (LLM)-based autonomous agents is reshaping the digital landscape toward an emerging Agentic Web, where increasingly specialized agents must collaborate to accomplish complex tasks. However, existing collaboration paradigms are constrained to message passing, leaving execution environments as isolated silos. This creates a context gap: agents cannot directly manipulate files or invoke tools in a peer's environment, and must instead resort to costly, error-prone environment reconstruction. We introduce the Agent Workspace Collaboration Protocol (AWCP), which bridges this gap through temporary workspace delegation inspired by the Unix philosophy that everything is a file. AWCP decouples a lightweight control plane from pluggable transport mechanisms, allowing a Delegator to project its workspace to a remote Executor, who then operates on the shared files directly with unmodified local toolchains. We provide a fully open-source reference implementation with MCP tool integration and validate the protocol through live demonstrations of asymmetric collaboration, where agents with complementary capabilities cooperate through delegated workspaces. By establishing the missing workspace layer in the agentic protocol stack, AWCP paves the way for a universally interoperable agent ecosystem in which collaboration transcends message boundaries. The protocol and reference implementation are publicly available at https://github.com/SII-Holos/awcp.

翻译：基于大语言模型（LLM）的自主智能体的快速发展正在重塑数字格局，推动着新兴的智能体网络（Agentic Web）的形成，其中日益专业化的智能体必须通过协作来完成复杂任务。然而，现有的协作范式局限于消息传递，导致执行环境成为相互隔离的孤岛。这造成了语境鸿沟：智能体无法直接操作对等智能体环境中的文件或调用其工具，而必须诉诸于成本高昂且容易出错的环境重建。本文提出了智能体工作空间协作协议（AWCP），该协议通过受“一切皆文件”的Unix哲学启发的工作空间临时委托机制来弥合这一鸿沟。AWCP将轻量级的控制平面与可插拔的传输机制解耦，允许委托方（Delegator）将其工作空间投射到远程执行方（Executor），后者随后可以使用未经修改的本地工具链直接对共享文件进行操作。我们提供了一个完全开源的参考实现，集成了MCP工具，并通过非对称协作的现场演示验证了该协议，演示中具有互补能力的智能体通过委托的工作空间进行合作。通过在智能体协议栈中建立缺失的工作空间层，AWCP为构建一个普遍可互操作的智能体生态系统铺平了道路，在该生态系统中，协作超越了消息传递的边界。协议及参考实现已在 https://github.com/SII-Holos/awcp 公开。