Quality Assurance (QA) is a critical aspect for a company whose product is an application directly used by customers. Traditional QA automation often struggles with dynamic interfaces and evolving scenarios, and it cannot effectively handle cases where identifying issues requires reasoning or contextual understanding. This thesis explores the integration of Large Language Models (LLMs) in a multi-modal setup to enhance QA automation, leveraging their contextual understanding of images and ability to extract meaningful information. The research focuses on three key phases: the experimentation phase to assess the current capabilities of state-of-the-art models in image analysis, the implementation of tests using real cases within the company's application, and the exploration of AI Agents to autonomously navigate the application and extract relevant information.