The paper "Agent AI: Surveying the Horizons of Multimodal Interaction" explores the emerging field of Agent AI, which aims to create interactive systems capable of perceiving and acting in various domains and applications. The authors define Agent AI as a class of systems that can process visual stimuli, language inputs, and other environmentally grounded data, and produce meaningful embodied actions. They emphasize the importance of grounding these systems in physical and virtual environments to facilitate the processing and interpretation of visual and contextual data, enhancing the sophistication and context-awareness of AI systems. The paper discusses the integration of large foundation models (LLMs and VLMs) into Agent AI, highlighting their role in improving the performance of agents in complex tasks. It addresses challenges such as hallucinations, biases, data privacy, interpretability, and regulation, and proposes solutions to mitigate these issues. The authors also introduce a new paradigm for training Agent AI, including the use of agent tokens to reserve specific subspaces for agentic behaviors, and a unified agent multi-modal transformer model that integrates visual, language, and agent tokens. The paper categorizes different types of agents, such as generalist agents, embodied agents, simulation and environment agents, generative agents, and knowledge and logical inference agents. It explores applications in gaming, robotics, healthcare, and multimodal interactions, and discusses the potential for continuous learning and self-improvement through interactions with the environment and users. The authors also propose new datasets and leaderboards for evaluating Agent AI systems and address ethical considerations and societal impacts. Overall, the paper provides a comprehensive overview of the current state and future directions of Agent AI, emphasizing its potential to revolutionize various industries and improve human experiences.The paper "Agent AI: Surveying the Horizons of Multimodal Interaction" explores the emerging field of Agent AI, which aims to create interactive systems capable of perceiving and acting in various domains and applications. The authors define Agent AI as a class of systems that can process visual stimuli, language inputs, and other environmentally grounded data, and produce meaningful embodied actions. They emphasize the importance of grounding these systems in physical and virtual environments to facilitate the processing and interpretation of visual and contextual data, enhancing the sophistication and context-awareness of AI systems. The paper discusses the integration of large foundation models (LLMs and VLMs) into Agent AI, highlighting their role in improving the performance of agents in complex tasks. It addresses challenges such as hallucinations, biases, data privacy, interpretability, and regulation, and proposes solutions to mitigate these issues. The authors also introduce a new paradigm for training Agent AI, including the use of agent tokens to reserve specific subspaces for agentic behaviors, and a unified agent multi-modal transformer model that integrates visual, language, and agent tokens. The paper categorizes different types of agents, such as generalist agents, embodied agents, simulation and environment agents, generative agents, and knowledge and logical inference agents. It explores applications in gaming, robotics, healthcare, and multimodal interactions, and discusses the potential for continuous learning and self-improvement through interactions with the environment and users. The authors also propose new datasets and leaderboards for evaluating Agent AI systems and address ethical considerations and societal impacts. Overall, the paper provides a comprehensive overview of the current state and future directions of Agent AI, emphasizing its potential to revolutionize various industries and improve human experiences.