AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
AgentCo-op introduces a novel framework for synthesizing multi-agent workflows using retrieval-based methods. This approach enhances the interoperability of AI agents by leveraging existing resources effectively.
The research paper "AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows" proposes an innovative framework for creating and managing complex AI agent systems. This approach utilizes retrieval-based synthesis, enabling seamless interaction and cooperation between various AI agents. The paper was authored by Shuaike Shen and four collaborators.
AgentCo-op focuses on improving the interoperability of multi-agent workflows. By synthesizing these workflows through retrieval, it allows AI systems to more efficiently access and utilize existing knowledge and tools. This method is crucial for developing robust and adaptable AI solutions in diverse applications.
The paper is available in PDF format and was submitted to arXiv on May 19, 2026. This work is accessible through arXiv's platform, which provides various bibliographic and citation tools, including Google Scholar and Semantic Scholar, for researchers to explore related content and track its impact.
arXivLabs, an experimental projects framework, supports initiatives like AgentCo-op by fostering collaboration between individuals and organizations. It ensures that new features and research adhere to core values of openness, community, excellence, and user data privacy, maintaining a valuable resource for the scientific community.
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