The MINIM framework enhances privacy for LLM-powered agents by minimizing unnecessary UI data transmission. It uses a dual-score system to assess the sensitivity and necessity of UI elements, ensuring that only essential information is shared. This approach reduces the risk of sensitive data leaks while maintaining task-critical context. Its implementation could significantly improve user privacy in digital environments reliant on agent interactions.