Scientific computing in the age of agentic AI
Scientific computing faces challenges due to outdated software that struggles to keep up with data growth, leading to inefficient workflows and maintenance. AI agents are addressing these issues by reducing engineering costs and enabling faster development, allowing researchers to focus more on discovery. The case studies show that agents improve software efficiency and maintenance, though long-term responsibility and stewardship remain critical.
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