Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
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.
Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 54 of its 78 references that are in the map. First posted on arXiv 12 Mar 2025.
Builds on
54 of 78 references.
Closest ancestors · 7
Same direction, newest first
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Other references · 47
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