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

Abstract

New possibilities for automating significant parts of the scientific research lifecycle have emerged as a result of the rapid development of large language models (LLMs), retrieval-augmented generation (RAG), autonomous agents, and tool-using artificial intelligence. In most cases, independent literature reviews, the formulation of hypotheses, the design of experiments, the analysis of data, and the validation of findings are necessary components of traditional scientific research. Even though individual stages of this process have been supported by AI more and more, most of the systems that are currently in use are still task-specific and require a lot of human intervention. A multi-agent framework for autonomous scientific research is proposed in this paper. In this framework, specialized artificial intelligence agents collaborate to perform data analysis, critical evaluation, validation, hypothesis generation, experimental design, literature discovery, and knowledge retrieval. Retrieval-augmented generation, external scientific databases, computational tools, experiment simulators, and verification agents are all part of the proposed framework. The use of independent critics and validation agents to reduce hallucinations, unsubstantiated hypotheses, methodological errors, and reproducibility issues is a key component of the framework. Hypotheses are generated from evidence, experiments are designed to test those hypotheses, results are analyzed, and conclusions are independently verified before being accepted in the closed-loop research process that is followed by the proposed architecture. An evaluation framework based on scientific validity, novelty, factuality, reproducibility, experimental efficacy, citation accuracy, and human expert agreement is also presented in the paper. Developing autonomous scientific research systems while maintaining human oversight at crucial decision points is made possible by the framework's conceptual foundation. The study argues that agentic AI should support evidence-based scientific discovery by acting as an auditable collaborative research ecosystem rather than simply replacing individual research tasks.