# Agentic software engineering

This article by Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov and Jørn Kristiansen argues that, following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, attention should shift from forecast output to the working practices that make prediction systems possible, and that machine learning and recent digital technologies will reshape the forecasting value chain — how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services; it discusses six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification

This article by Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov and Jørn Kristiansen argues that, following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, attention should shift from forecast output to the working practices that make prediction systems possible, and that machine learning and recent digital technologies will reshape the forecasting value chain — how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services; it discusses six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification