Retrieval-Augmented Generation (RAG) has become a foundational paradigm for integrating AI agents with external knowledge. However, current RAG models remain largely constrained by static retrieval pipelines and limited capacity for adaptive reasoning over hierarchical knowledge structures. As AI agents increasingly operate in dynamic, information-rich environments, there is a growing need for models that can reason across modalities while continuously evolving their knowledge representations. We introduce HFlow, a self-evolving multimodal agentic RAG framework grounded in hypergraph representations and hyperbolic flow-based reasoning. In our formulation, heterogeneous modalities, including text, images, audio, and structured data, are modelled as nodes within a multimodal hypergraph, while hyperedges capture higher-order semantic and cross-modal relationships. By embedding this structure in hyperbolic space, the framework preserves hierarchical and compositional knowledge. This work proposes a shift from static retrieval to continuous knowledge navigation, where reasoning emerges through geometry-aware traversal of multimodal knowledge manifolds. The proposed framework unifies retrieval, reasoning, and adaptation within a single agentic architecture, offering a new direction for scalable, context-aware AI models. We discuss early empirical evidence demonstrating improved robustness and reasoning flexibility compared to conventional Euclidean and pipeline-based multimodal RAG approaches and outline future opportunities for self-improving knowledge agents.