The emergence of Agentic AI underscores the growing need to tailor models to diverse applications, as highlighted in recent research on personalized federated learning (PFL). This approach enables multiple clients to collaboratively share knowledge while preserving their privacy, addressing a key limitation of traditional methods that often fail to consider the unique requirements of individual clients. By focusing on model adaptation for specific tasks, PFL moves beyond one-size-fits-all solutions, enhancing performance across heterogeneous multi-modal clients. The collaborative nature of PFL supports privacy-conscious environments, aligning with ongoing developments in federated learning that emphasize secure data sharing. This shift reflects a broader trend in AI toward more nuanced and client-specific model personalization, which is critical for advancing applications in varied and sensitive contexts. Overall, personalized federated learning represents a significant step forward in balancing collaboration and privacy in AI model development.