Can AI agents automate the creation of recovery mechanisms for scientific programs following system failures? This approach was developed to optimize the workflow. The problem is that a supercomputer might become unable to continue solving a task at a certain point during execution. Without a recovery mechanism for unexpected shutdowns, the entire computation could be lost, necessitating a complete restart—which is inefficient in terms of both time and resource utilization. The solution: checkpoints and resuming from the last saved state (the same checkpoints that saved us in video games—think parkour or survival horror—can now save other types of computations 🙃). AI agents analyzed the code structure, identified the data needed to resume operations after a failure, created checkpoints, automatically tested the system by simulating failures (forced shutdowns), and repeated the cycle until successful execution. https://t.co/lb3vJxL51f