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

Key points

• Problem: LLM-driven agents have made strides in automating general research workflows such as citation management, document discovery, and academic survey generation, but the authors note that these systems often lack the domain-specific focus and compliance-driven rigor needed in fields like biomedicine, where structured assessment of literature is critical. For example, Agent Laboratory showed high success rates in data preparation, experimentation, and report writing but its performance dropped significantly in the literature review phase, and system reliability, reproducibility, and ethical governance continue to pose significant hurdles.
• Scope: This is a survey of agentic AI for scientific discovery that covers the definition of agents and single-agent versus multi-agent designs, a taxonomy of systems, literature review agents, discovery systems in chemistry, biology, materials science, general science, and machine learning, implementation tools, datasets, evaluation metrics, and open challenges. The authors split the research lifecycle into four steps, ideation, experiment design and execution, data analysis and interpretation, and paper writing and dissemination, and describe what agents take on at each step.
• Taxonomy: Fully autonomous systems automate end-to-end workflows with minimal human intervention, including Coscientist, a GPT-4-powered agent that plans, designs, and executes chemical experiments, ChemCrow, which integrates 18 expert-designed tools, and ProtAgents for protein design. Human-AI collaborative systems work with researchers, including Virtual Lab, which organizes interdisciplinary research through team meetings and individual tasks, BioPlanner, which designs experimental protocols and leaves their execution to researchers, CALMS (Context-Aware Language Model for Science), a lab assistant that interacts with scientists and laboratory instruments, and Agent Laboratory, which takes human-provided ideas through literature review, experimentation, and report writing. The authors summarize that autonomous systems are efficient on well-defined, repetitive tasks but may struggle with tasks that need creativity or domain intuition, while collaborative systems rely on human oversight, data quality, and interpretability and still require manual validation and execution.
• Cases: In chemistry, the survey describes LLM-RDF, which splits literature search, experimental design, reaction optimization, and data analysis across six LLM-based agents and demonstrated end-to-end synthesis automation on aerobic alcohol oxidation, and the robotic system Organa, which achieved results comparable to human chemists in electrochemistry while reducing execution time by over 20%. In biology, it describes CellAgent, a multi-agent system whose Planner, Executor, and Evaluator roles collaborate, reaching a 92% task completion rate in single-cell RNA sequencing data analysis. These figures are the survey's summaries of the original papers of each system.
• Tools and evaluation resources: The survey lists implementation tools such as AutoGen, a framework for managing multi-agent systems, MetaGPT, which assigns roles to agents for software development, Letta, an open-source framework for deploying persistent agents as services, and LangChain and AutoGPT. Its dataset table includes LAB-Bench for reasoning and planning in biological research, MoleculeNet for molecular property prediction, ZINC for virtual screening in drug discovery, MaScQA for materials science question answering, and ICLR 2022 OpenReview data for evaluating an automated paper reviewer. For metrics, reasoning and planning typically use accuracy, task completion rates, and response coherence, prediction and discovery use precision, recall, and prediction error, and the authors note that standardized metrics for complex tasks such as multi-agent cooperation in experimental automation and hypothesis generation are still in development.
• Limits: The authors see literature review as a significant challenge across nearly all approaches: in Agent Laboratory the literature review phase had the highest failure rate compared with the data preparation, experimentation, and report writing phases, and ResearchAgent and The AI Scientist lack structured literature review. They note that emerging areas such as materials discovery and entire research process automation still need comprehensive benchmarks that assess real-world impact and adaptability. As risks they cite compounding errors in highly autonomous agents with little human oversight, possible deviations from established protocols or safety measures in experimental automation, and agents tied to physical systems misreading situations in ways that lead to unexpected escalations or system failures, and they stress AI governance and human oversight.

Paper links

External research summaries. These are not HDATF publications or measured product results.