Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneously. We present Agentic-J, a containerised, multi-agent AI assistant, primarily for ImageJ/Fiji that enables biologists to specify analysis tasks in natural language, from nuclei segmentation and cell tracking to multi-condition quantification. The agent generates executable scripts organised into a documented project structure, so every analysis decision is traceable and the workflow can be reproduced or shared. The specialised sub-agents handle plugin management, code generation, debugging, quality assurance, and statistical reporting. In this paper we introduce the system's design, demonstrate real biological microscopy image analysis workflows, and detailed the technical implementation.

翻译：生物学图像分析日益需要整合异构工具、编程环境及领域知识，而鲜有研究人员能同时掌握这些技能。我们提出Agentic-J——一款面向ImageJ/Fiji的容器化多智能体AI助手，可使生物学家以自然语言描述分析任务（包括细胞核分割、细胞追踪及多条件定量分析）。该智能体生成的可执行脚本组织于结构化文档化项目文件中，确保每项分析决策可追溯，工作流程可复现与共享。专业子智能体负责插件管理、代码生成、调试、质量保证及统计报告。本文介绍系统设计，演示真实生物显微图像分析工作流，并详述技术实现。