The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we address critical challenges, such as literature review automation, system reliability, and ethical concerns, while outlining future research directions that emphasize human-AI collaboration and enhanced system calibration.

翻译：智能体人工智能与科学发现的融合标志着研究自动化的新前沿。这些具备推理、规划与自主决策能力的人工智能系统，正在彻底改变科学家进行文献综述、提出假设、开展实验和分析结果的方式。本综述全面概述了面向科学发现的智能体人工智能，对现有系统与工具进行分类，并重点介绍了在化学、生物学和材料科学等领域的最新进展。我们讨论了关键评估指标、实现框架和常用数据集，以提供对该领域现状的详细理解。最后，我们探讨了诸如文献综述自动化、系统可靠性和伦理问题等关键挑战，同时展望了强调人机协作与增强系统校准的未来研究方向。