Agentic-J: An AI Agent for Biological Microscopy Image Analysis
For biologists lacking programming expertise, Agentic-J lowers the barrier to advanced image analysis by automating tool integration and workflow documentation.
Agentic-J is a multi-agent AI assistant that enables biologists to perform complex microscopy image analysis tasks via natural language, generating executable, documented scripts for ImageJ/Fiji. It handles nuclei segmentation, cell tracking, and multi-condition quantification with traceable, reproducible workflows.
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.