Recommender systems are essential components of e-commerce, streaming media, and social networks, driving significant product and business impact. At Netflix, these systems connect members with relevant content at the right time. The recommendation foundation model has made substantial progress in understanding user preferences, but there is an opportunity to further enhance its capabilities. By extending the foundation model to incorporate the prediction of underlying user intents, the model can enrich its understanding of user sessions beyond next-item prediction. Recent research has highlighted the importance of understanding user intent in online platforms, leading to more accurate and personalized recommendations. FM-Intent, a novel recommendation model, captures a user's latent session intent using short-term and long-term implicit signals as proxies, then leverages this intent prediction to improve next-item recommendations. The model establishes a hierarchical relationship between intent predictions and next-item recommendations, creating a more coherent and effective recommendation pipeline. FM-Intent makes three key contributions: a novel recommendation model, a hierarchical multi-task learning approach, and comprehensive experimental validation showing significant improvements over state-of-the-art models. FM-Intent has been successfully integrated into Netflix's recommendation ecosystem and can be leveraged for several downstream applications, including personalized UI optimization, analytics, and enhanced recommendation signals.