Agentic Discovery: Closing the Loop with Cooperative Agents

Abstract--As data-driven methods, artificial intelligence (AI), and automated workflows accelerate scientific tasks, we see the rate of discovery increasingly limited by human decision-making tasks such as setting objectives, generating hypotheses, and designing experiments. We postulate that cooperative agents are needed to augment the role of humans and enable autonomous discovery . Realizing such agents will require progress in both AI and infrastructure. This situation is emblematic of broader transformations associated with the fourth and fifth paradigms of science, which capture the shift towards data-intensive methods and artificial intelligence, respectively, as integral aspects of scientific exploration [1], [2]. Fields ranging from astrophysics to social sciences now rely on vast datasets, AI models, and computational methods to drive innovation. Hence we face the challenge of not just managing data and building models, but also building systems that enable researchers to integrate and utilize data and models at scale. Current approaches to integrating data-intensive workflows and AI methods have yielded successes, but use techniques that result in siloed solutions that fail to scale or generalize. This paradigm shift demands more than building increasingly sophisticated tools; it calls for a fundamental rethinking of how science is conducted.