发表机构
Lawrence Berkeley National Laboratory; California Institute of Technology; SIB Swiss Institute of Bioinformatics（劳伦斯伯克利国家实验室; 加州理工学院; SIB瑞士生物信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对智能体AI应用于生物数据库整理的障碍，构建了基于JupyterHub与Claude Code的云环境及含四个模块的研讨会，证明其可提升分布式社区的智能体AI整理能力。
AI 中文摘要
智能体AI有潜力加快生物数据库和知识库的整理工作，但受限于诸多挑战与障碍，包括智能体的获取途径及相关培训不足。本文介绍了我们如何通过部署基于云的智能体环境，并为基因本体论联盟（Gene Ontology Consortium）开发交互式培训研讨会，来应对和缓解这些挑战。我们的智能体辅助整理云环境基于JupyterHub平台构建，使用Claude Code作为通用管控工具，这使得整理人员可通过浏览器运行的终端与智能体会话交互，还具备通过单一API网关集中访问的额外优势，无需参与者管理订阅或在本地安装软件。我们创建了四个培训模块，先引导参与者掌握基础的智能体工具使用，再逐步过渡到利用现有GO-CAM（GO因果活动模型）整理工具开展智能体生物通路整理工作。共有37名参与者参与了为期四小时的研讨会。我们从该研讨会得出的关键结论是，在分布式科学社区中构建智能体AI的社区能力，核心在于解决访问权限、工作流程设计及培训问题；消除技术障碍、逐步引入相关能力、将练习建立在熟悉的整理任务基础上，并让整理人员直接评估智能体输出，可为构建分布式科学社区的共享智能体AI能力提供可行路径。
英文摘要
Agentic AI has the potential to accelerate curation of biological databases and knowledge bases. However, uptake has been hindered by a number of challenges and obstacles, including access to agents and appropriate training. Here we describe how we have attempted to address and mitigate these challenges and obstacles through the deployment of a cloud-based agentic environment, and the development of an interactive training workshop for the Gene Ontology Consortium. Our cloud environment for agentic-assisted curation was based on the JupyterHub platform, and utilized Claude Code as a universal harness. This allows curators to interact with an agent session through a terminal running in the browser, and has additional benefits such as centralization of access through a single API gateway, removing the need for participants to manage subscriptions or install software locally. We created four training modules, walking participants through basic agentic tool use first and then working up to agentic biological pathway curation using the existing GO-CAM (GO Causal Activity Model) curation tool. Thirty-seven participants took part in the four-hour workshop. Our key takeaway from this workshop is that building community capability with agentic AI is primarily a problem of access, workflow design, and training. Removing technical barriers, introducing capabilities gradually, grounding exercises in familiar curation tasks, and giving curators direct experience evaluating agent output can provide a practical route toward building shared agentic AI capability in distributed scientific communities.
