# Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions

**Authors:** Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes, Christina Mack  
**arXiv:** [2503.08979](https://arxiv.org/abs/2503.08979)  
**Date:** 2025-03-12  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2503.08979v1/

## Key points

- Integration of Agentic AI into scientific discovery is a groundbreaking advancement in research automation
- Agentic AI technology can reason, plan, and make autonomous decisions, transforming literature review, hypothesis generation, experiment execution, and result analysis
- Authors Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes and Christina Mack categorize existing AI systems and tools for scientific discovery
- Recent advancements in chemistry, biology, and materials science are highlighted in the survey
- Survey covers evaluation metrics, implementation frameworks, and commonly used datasets to provide detailed insight into the current landscape of Agentic AI in scientific research
- Critical challenges addressed include literature review automation hurdles, system reliability concerns, and ethical considerations
- Future research directions emphasize human-AI collaboration and enhanced system calibration for further progress
- Agentic AI has transformative potential in driving innovation within scientific exploration while requiring continuous development to overcome challenges and ensure ethical deployment

## Summary

The integration of Agentic AI into scientific discovery represents a groundbreaking advancement in research automation. This cutting-edge technology is equipped with the ability to reason, plan, and make autonomous decisions, revolutionizing the way scientists engage in literature review, hypothesis generation, experiment execution, and result analysis. In their comprehensive survey on Agentic AI for scientific discovery, authors Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes and Christina Mack categorize existing AI systems and tools while highlighting recent advancements in fields like chemistry, biology and materials science. The survey delves into essential aspects such as evaluation metrics, implementation frameworks and commonly utilized datasets to provide a detailed insight into the current landscape of Agentic AI in scientific research. Moreover,the authors address critical challenges encountered in this domain including literature review automation hurdles,system reliability concerns,and ethical considerations.They also outline future research directions that emphasize the importance of human-AI collaboration and enhanced system calibration for further progress.This study underscores the transformative potential of Agentic AI in driving innovation within scientific exploration while emphasizing the need for continuous development to overcome existing challenges and ensure ethical deployment of these advanced technologies.