Researchers have introduced Video-DeepResearch, a multimodal deep research agent designed to process continuous video streams, requiring precise spatiotemporal grounding and open-web exploration. Initial assessments have uncovered two significant limitations in existing models: modality bias, where agents prioritize textual search over visual tools, and parametric knowledge leakage1. This development is crucial as it highlights the need for more sophisticated multimodal agents that can effectively integrate visual and textual information. The Video-DeepResearch agent aims to address these bottlenecks by extending multimodal agents from static images to dynamic video streams. This advancement has significant implications for the field of artificial intelligence, as it enables the creation of more advanced and human-like agents. The introduction of Video-DeepResearch marks a significant step towards developing more robust and efficient multimodal deep research agents, which is essential for practitioners seeking to improve the performance of AI systems that interact with complex and dynamic data, so what matters most is how this development will influence the design of future multimodal agents.