ScienceBuddy AI agent improvement
This approach solves the common problem of AI agent improvements being temporary, as fixes in chat typically don't transfer to new tasks. For developers building AI agents, this method provides a concrete framework to systematically improve performance by saving user corrections as test cases and iteratively upgrading both prompts and models. The research offers a practical solution for creating more reliable scientific AI assistants that can learn from interactions.
