Agentic-J: An AI Agent for Biological Microscopy Image Analysis

2026-06-01

AI Summary

Biological microscopy image analysis typically demands the integration of heterogeneous tools, programming expertise, and domain-specific knowledge, presenting a high barrier to entry. This work proposes Agentic-J, a containerized multi-agent AI assistant designed for the ImageJ/Fiji platform that enables users to specify analytical tasks in natural language and automatically generates well-structured, traceable, and reproducible analysis scripts. The system orchestrates specialized sub-agents to collaboratively manage plugin selection, code generation, debugging, quality assurance, and statistical reporting, thereby achieving end-to-end transparent automation. Experimental results demonstrate complete workflows—from nuclear segmentation and cell tracking to multi-condition quantification—significantly enhancing reproducibility and shareability of image analysis pipelines.

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.

Problem

• biological image analysis
• heterogeneous tools
• domain knowledge
• workflow reproducibility
• natural language interface

Innovation

• multi-agent AI
• natural language interface
• reproducible image analysis
• containerized workflow
• ImageJ/Fiji automation