Scientific computing in the age of agentic AI

Scientific computing is a core pillar of modern research across academia and industry. Yet the software needed to analyze scientific information has struggled to keep pace with the rapid rate of data generation. Many widely used research tools began as code accompanying a research paper, built by small academic teams with limited engineering experience and minimal time for packaging, testing, optimization, or long-term support. The result is scientific infrastructure that often depends on slow, fragile workflows requiring constant maintenance. These constraints impede the pace of discovery.

AI agents are beginning to change that equation. By lowering the costs of engineering work and taking on tedious implementation tasks, they can help researchers prototype ideas more quickly, pursue projects that were previously impractical, and more easily maintain software over the long term. As a result, scientific software becomes more efficient and better maintained, freeing researchers to spend more time on discovery.

We’re sharing an exploratory field report of eight agent-assisted scientific computing projects primarily in the life sciences; five using Codex alone, and three using a combination of Codex and Claude Code. The report brings together case studies written by the teams behind each project and identifies recurring themes. The projects range from routine maintenance and targeted optimization to large-scale language migrations and GPU-native redesigns. Contributors report that agents significantly accelerated software development and maintenance, in some cases helping small teams take on work that would otherwise have required far more time or specialized engineering support. But they also highlight the persistent challenge of establishing clear, long-term responsibility and stewardship of the resulting tools.