AWCP: A Workspace Delegation Protocol for Deep-Engagement Collaboration across Remote Agents
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 27 of its 42 references that are in the map. First posted on arXiv 24 Feb 2026.
Builds on
27 of 42 references.
Closest ancestors · 10
Same direction, newest first
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- 2025AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications22 Aug
- 2025A Survey of AI Agent Protocols23 Apr
- 2023A Survey on Large Language Model based Autonomous Agents22 Aug
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar
Other references · 17
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
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- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
- 2023ChatDev: Communicative Agents for Software Developmentcited by 893
- 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
- 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systemscited by 118
- 2025A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)cited by 98
- 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 54
- 2025Agentic Web: Weaving the Next Web with AI Agentscited by 43
- 2025Agent Network Protocol Technical White Papercited by 24
- 2025Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directionscited by 22
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- 2025CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generationcited by 10
- 2025EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaborationcited by 9
- 2025AgentGit: A Version Control Framework for Reliable and Scalable LLM-Powered Multi-Agent Systemscited by 8
- 2026Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineeringcited by 6
- 2025EnvX: Agentize Everything with Agentic AIcited by 2
Built on by
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