Give an AI agent a model overnight. By morning, you may have a full experiment log and a better model.
This is the idea behind Andrej Karpathy’s Autoresearch. The agent changes the training code, runs an experiment, evaluates the result, and keeps the change only when performance improves.
Its relevance goes beyond model training. It offers a practical pattern for systems that can test, learn, and improve continuously.
Consider a data pipeline that becomes slower or less reliable as data volume grows and source structures change. An agent could test different methods of cleaning, transformation, and processing in a controlled environment. A change would be accepted only if it improves execution time without reducing data quality.
The same approach can support operational planning. Different resource allocation or maintenance schedules can be tested against cost, downtime, and output quality before being introduced into the actual workflow.
At Hiwa AI, we apply this approach by building controlled improvement loops around enterprise data and workflows. Each proposed change is tested in a limited environment, measured against business and technical criteria, and accepted only when the improvement is valid, traceable, and safe.
This requires a clear objective, reliable metrics, a limited action space, rollback mechanisms, and human oversight. Without these controls, a system may improve one metric while increasing cost or risk elsewhere.
Autoresearch is not simply about automating work. It is about automating experimentation, evaluation, and continuous improvement.
Original source: https://lnkd.in/eAEvVK4s