You can automatically optimize how agents interact with tools by having them learn from rollout feedback—AutoDesign shows this approach consistently improves performance across different models and scales to real-world tasks.
AutoDesign is a framework that automatically improves how AI agents interact with tools by learning from feedback. It treats complex tasks like converting academic papers into posters as a long-horizon process where a meta-optimizer guides a code agent to refine its interaction patterns.