Agentic-J: An AI Agent for Biological Microscopy Image Analysis
2026-06-01 • Multiagent Systems
Multiagent SystemsArtificial IntelligenceComputer Vision and Pattern Recognition
AI summaryⓘ
The authors created Agentic-J, an AI helper designed to work with biological image analysis software like ImageJ/Fiji. It lets biologists describe their image analysis tasks using everyday language, and then the system writes and organizes the necessary code automatically. This helps keep the analysis steps clear and repeatable. Different parts of the AI handle tasks like managing plugins, fixing code issues, and creating reports. The authors explain how their system works and show examples of it analyzing microscope images.
ImageJFijibiological image analysisnuclei segmentationcell trackingmulti-agent AIcontainerisationcode generationworkflow reproducibilityplugin management
Authors
Lukas Johanns, Marilin Moor, Davide Panzeri, Yu Zhou, Xinyi Chen, Nora F. K. Pauly, Zixuan Pan, Matthias Gunzer, Andreas Müller, Yiyu Shi, Hedi Peterson, Jianxu Chen
Abstract
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.