AGENTIC AI FOR AUTONOMOUS SCIENTIFIC RESEARCH: A MULTI-AGENT FRAMEWORK FOR HYPOTHESIS GENERATION, EXPERIMENT DESIGN AND VALIDATION

Abstract

The rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG), autonomous agents, and tool-using artificial intelligence has created new possibilities for automating significant portions of the scientific research lifecycle. Traditional scientific research generally requires researchers to independently conduct literature reviews, formulate hypotheses, design experiments, analyze data, and validate findings. Although artificial intelligence has increasingly supported individual stages of this process, most existing systems remain task-specific and require substantial human intervention. This paper proposes a multi-agent framework for autonomous scientific research in which specialized artificial intelligence agents collaboratively perform literature discovery, knowledge retrieval, hypothesis generation, experimental design, data analysis, critical evaluation, and validation. The proposed framework combines LLM-based reasoning with retrieval-augmented generation, external scientific databases, computational tools, experiment simulators, and verification agents. A central feature of the framework is the use of independent critic and validation agents to reduce hallucinations, unsupported hypotheses, methodological errors, and reproducibility problems. The proposed architecture follows a closed-loop research process in which hypotheses are generated from evidence, experiments are designed to test those hypotheses, results are analyzed, and conclusions are subjected to independent verification before being accepted. The paper further proposes an evaluation framework based on scientific validity, novelty, factuality, reproducibility, experimental efficiency, citation correctness, and human expert agreement. The framework provides a conceptual foundation for developing autonomous scientific research systems while maintaining human oversight at critical decision points. The study argues that agentic AI should not simply replace individual research tasks but should function as an auditable collaborative research ecosystem capable of supporting evidence-based scientific discovery.