Title: Agentic-J: Leveraging AI Agents for Biological Microscopy Image Analysis

Abstract: The complexity of modern biological image analysis often requires researchers to master a disparate array of tools, coding environments, and specialized domain knowledge—a combination that is rarely within the grasp of any single individual. To address this challenge, we introduce Agentic-J, a containerized, multi-agent artificial intelligence assistant designed primarily for the ImageJ/Fiji ecosystem. This system empowers biologists to define their analytical objectives using natural language, covering tasks such as cell tracking, nuclei segmentation, and quantification across multiple conditions. Agentic-J produces executable scripts that are structured within a documented project framework, ensuring that every analytical choice is traceable and that the resulting workflows are both reproducible and shareable. The platform relies on specialized sub-agents to manage plugins, generate code, perform debugging, ensure quality assurance, and handle statistical reporting. This paper outlines the architectural design of the system, illustrates its application through real-world biological microscopy analysis workflows, and provides a detailed account of its technical implementation.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC