AI agents can be efficiently personalized to individual users by learning from their iterative feedback during real work, rather than requiring explicit upfront specifications of what success looks like.
This paper presents TAHI, a method that adapts AI agents to individual users by learning from their feedback during repeated interactions. Instead of training on generic population data, the system uses a user's preferences and corrections across multiple tasks to personalize the agent's behavior and create custom evaluation rubrics.