Artificial intelligence (AI) has the potential to accelerate scientific discovery, yet many domain scientists face significant barriers when attempting to incorporate AI into their research. These challenges include a lack of formal training, limited access to computational resources, difficulty selecting appropriate models, and fragmented workflows. Through interviews with eleven postdoctoral researchers across diverse scientific fields at UC San Diego, we identify common pain points and recurring questions that arise throughout the scientific research pipeline. In response, we propose a conceptual framework, called Agents of Intelligence: modular, context-aware AI agents designed to assist researchers with tasks such as model recommendation, data preparation, infrastructure coordination, and interpretation of results. By embedding AI expertise into composable workflows, these agents aim to make AI more accessible, systematic, and transferable. We describe how this framework can be instantiated within the National Data Platform (NDP) and provide a testbed for real-world integration and evaluation.

Distributed computing systems are essential for meeting the demands of modern HPC applications, yet transitioning from single-system to distributed environments presents significant challenges. Resource misallocation in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable hardware for applications using a contextual multi-armed bandit algorithm. We evaluated BanditWare on two workflow applications: BurnPro3D (a web-based platform for fire science), and a matrix multiplication application. Designed for seamless integration with the National Data Platform (NDP), BanditWare enables users of all experience levels to optimize resource allocation efficiently.

Scientific workflow applications have become mainstream and their automated and efficient execution on large-scale compute platforms is the object of extensive research and development. For these efforts to be successful, a solid experimental methodology is needed to evaluate workflow algorithms and systems. A foundation for this methodology is the availability of realistic workflow instances. Dozens of workflow instances for a few scientific applications are available in public repositories. While these are invaluable, they are limited: workflow instances are not available for all application scales of interest. To address this limitation, previous work has developed generators of synthetic, but representative, workflow instances of arbitrary scales. These generators are popular, but implementing them is a manual, labor-intensive process that requires expert application knowledge. As a result, these generators only target a handful of applications, even though hundreds of applications use workflows in production.In this work, we present WfChef, a framework that fully automates the process of constructing a synthetic workflow generator for any scientific application. Based on an input set of workflow instances, WfChef automatically produces a synthetic workflow generator. We define and evaluate several metrics for quantifying the realism of the generated workflows. Using these metrics, we compare the realism of the workflows generated by WfChef generators to that of the workflows generated by the previously available, hand-crafted generators. We find that the WfChef generators not only require zero development effort (because it is automatically produced), but also generate workflows that are more realistic than those generated by hand-crafted generators.