Towards an AI-driven scientific workflow

Modern scientific computing requires researchers to navigate a complex ecosystem of disconnected tools and technologies. A typical computational workflow follows sequential stages—data gathering, resource allocation, application deployment, and data analysis—each requiring distinct tools and manual management, diverting scientists from hypothesis-driven discovery to computational housekeeping. Rapidly evolving and diverse computing architectures intensify the problem, demanding specialized tools to meet High Performance Computing (HPC) needs for massive data volumes and extreme performance requirements. This requires researchers to adapt their workflows to new hardware and programming models, leading to productivity barriers as they must master dozens of tools before beginning scientific investigations.