The CVPR 2025 conference features research on Visual Agents, which represent a significant advancement in Visual AI and agentic AI. These agents enable systems to perceive, understand, and interact with visual interfaces like humans do. Recent advancements in foundational vision language models have provided the perceptual capabilities to tackle the long-standing challenge of GUI automation. The research wave matters now for Agentic AI as these capabilities align with the growing need for AI systems that can navigate the increasingly complex digital world on our behalf. Different teams have tackled distinct aspects of the visual agent challenge, including novel architectures, techniques for efficient processing, and methods for precise element grounding. Visual Agents are moving from academic curiosity to practical technology, with applications in GUI automation, navigation, and collaborative AI system design. The research has shown that these agents can achieve state-of-the-art performance on various benchmarks, including manipulation, gaming, navigation, UI control, and planning tasks. The key lessons for practitioners include leveraging a strong pretrained MLLM base, learning a flexible action representation, and training with a combination of large-scale supervised data from multiple domains and subsequent online reinforcement learning. Additionally, the research highlights the importance of prioritizing element grounding, knowledge retrieval, and multi-agent architecture in developing effective Visual Agents. The future of Visual Agents is moving from perception to interaction, enabling systems that can not only perceive but also meaningfully act within their environment, with applications in various domains such as GUI automation, navigation, and collaborative AI system design.