The Role of Agentic AI in Revolutionizing Biotechnology: An Overview | ||||
| International Journal for Biotech Research and Innovations | ||||
| Articles in Press, Accepted Manuscript, Available Online from 01 August 2025 | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/ijbraj.2025.368988.1002 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Nehal Alaa1; Mostafa Hesham1; Mariam Attia1; Radwa Kamal1; Saleh A. S. Alhammad2; Doha Ibrahim1; Gehan Safwat1; Ayman A. Diab1; Ahmed E. Gomaa  1
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| 1Prototyping Research Lab, Research, Development, and Innovation RDI, Faculty of Biotechnology, October Modern University for Science and Arts (MSA), Giza, Egypt. | ||||
| 2Advanced Generation School, Jeddah, Saudi Arabia. | ||||
| Abstract | ||||
| Agentic AI, a form of artificial intelligence capable of autonomous decision-making and self-directed actions, is emerging as a transformative force in biotechnology. Unlike traditional AI models that rely on predefined instructions, agentic AI can independently analyze complex biological data, optimize laboratory workflows, and drive scientific discovery with minimal human intervention. This autonomy enhances efficiency, reduces costs, and accelerates advancements in drug discovery, precision medicine, synthetic biology, and industrial bioprocessing. Despite its potential, the integration of agentic AI into biotechnology presents several challenges, including data bias, model interpretability, ethical concerns, and security risks. The lack of regulatory frameworks for AI-driven biological research further complicates its adoption. However, continuous advancements in AI-human collaboration, explainable AI, and interdisciplinary research are paving the way for safer and more effective applications. This review explores the fundamentals of agentic AI, its applications in various biotechnological fields, and the challenges associated with its deployment. Additionally, it examines emerging trends and future directions, including the convergence of agentic AI with quantum computing and nanotechnology. By addressing both the opportunities and risks, this article provides a comprehensive overview of how agentic AI is shaping the future of biotechnology. | ||||
| Keywords | ||||
| Agentic AI; Artificial Intelligence; Biotechnology; Drug Discovery; Precision Medicine; Synthetic Biology; Industrial Bioprocessing; Genomics; Laboratory Automation; AI Ethics; Machine Learning; Neural Networks; Deep Learning; Bioinformatics | ||||
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