A New Paradigm: Agentic AI for Scientific Discovery

Abstract

Artificial intelligence in science is undergoing a foundational change. Rather than serving as a passive analytical instrument — classifying images, predicting structures, spotting patterns — AI systems are beginning to act as autonomous research collaborators. These systems, built on large language models and tool-integrated architectures, can reason about experimental design, formulate strategies, execute multi-step workflows, and refine their approaches from empirical feedback. Often called “AI Scientists,” they participate across the full research lifecycle, from the seed of a hypothesis through to a draft manuscript. This article examines the emerging paradigm of agentic AI for scientific discovery. It traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research. A central concern of the analysis is the verification crisis — the growing gap between what these systems can produce and what they can prove. We compile quantitative evidence on failure rates, analyse competing frameworks for trustworthy agentic science (Chain-of-Evidence, Audit-Closed protocols, FEV, and structural FDR enforcement), and propose actionable standards for rigorous validation. The article closes with an assessment of the field’s limitations and a set of priority directions for making agentic science trustworthy at scale.