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Artificial Intelligence agents for biological research: a survey.

Cong Qi¹, Wenbo Wang², Siqi Jiang¹

¹Department of Computer Science, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, United States.

Briefings in Bioinformatics

|February 26, 2026

Summary

Artificial intelligence (AI) agents are advancing biological research beyond prediction to autonomous action. This survey synthesizes over 100 studies on biological AI agents, offering a framework for future development.

Keywords:

bioinformatics computational biology generative AI

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Area of Science:

Computational Biology
Bioinformatics
Artificial Intelligence in Life Sciences

Background:

Biological data and experimental complexity are rapidly increasing.
Current AI models primarily function as passive tools in research.
There is a growing need for AI systems capable of autonomous reasoning and action.

Purpose of the Study:

To systematically synthesize recent advancements in biological AI agents.
To provide a unified framework for understanding the landscape of biological AI.
To identify key challenges and future directions for agent-based biological research.

Main Methods:

Conducted a systematic review of over 100 representative studies on biological AI agents.
Developed a 5D taxonomy to organize existing research across task domains, architectures, interaction modes, evaluation, and resource integration.
Analyzed design patterns, emerging capabilities, and open challenges.

Main Results:

Identified key application areas including clinical analytics, molecular and drug design, multi-omics analysis, and knowledge discovery.
Highlighted the potential of agentic paradigms for adaptive and interactive biological analysis.
Cataloged common design patterns and emerging capabilities of biological AI agents.

Conclusions:

Biological AI agents offer a paradigm shift toward autonomous reasoning and action in life sciences.
Key challenges remain in reliability, privacy, scalability, and standardized evaluation.
This survey provides a roadmap for developing more robust, transparent, and collaborative agent-based systems for biological research.