arXiv:2504.10519v1 Announce Type: new
Abstract: AI Agents powered by Large Language Models are transforming the world through enormous applications. A super agent has the potential to fulfill diverse user needs, such as summarization, coding, and research, by accurately understanding user intent and leveraging the appropriate tools to solve tasks. However, to make such an agent viable for real-world deployment and accessible at scale, significant optimizations are required to ensure high efficiency and low cost. This paper presents a design of the Super Agent System. Upon receiving a user prompt, the system first detects the intent of the user, then routes the request to specialized task agents with the necessary tools or automatically generates agentic workflows. In practice, most applications directly serve as AI assistants on edge devices such as phones and robots. As different language models vary in capability and cloud-based models often entail high computational costs, latency, and privacy concerns, we then explore the hybrid mode where the router dynamically selects between local and cloud models based on task complexity. Finally, we introduce the blueprint of an on-device super agent enhanced with cloud. With advances in multi-modality models and edge hardware, we envision that most computations can be handled locally, with cloud collaboration only as needed. Such architecture paves the way for super agents to be seamlessly integrated into everyday life in the near future.
The Rise of Super Agents: Bridging the Gap Between AI and User Needs
AI Agents powered by Large Language Models have become an integral part of our daily lives. They have the potential to fulfill a wide range of user needs, from summarization and coding to research and much more. However, for these agents to be truly effective and accessible at scale, significant optimizations are required.
The Super Agent System, presented in this paper, aims to bridge the gap between user intent and agent capabilities. When a user prompt is received, the system first detects the intent behind the request. It then routes the request to specialized task agents equipped with the necessary tools or automatically generates agentic workflows. This process ensures that the agent can accurately understand user needs and efficiently solve tasks.
A key consideration in the design of the system is the deployment of AI assistants on edge devices such as phones and robots. This approach allows for faster response times and protects user privacy. However, the varying capabilities of different language models and the computational costs associated with cloud-based models pose challenges. To overcome this, the system explores a hybrid mode where the router dynamically selects between local and cloud models based on task complexity.
The introduction of an on-device super agent enhanced with cloud capabilities further enhances the potential of this architecture. With advancements in multi-modality models and edge hardware, a larger portion of computations can be handled locally, with cloud collaboration only utilized when necessary. This optimization not only improves efficiency and reduces costs but also paves the way for seamless integration of super agents into everyday life.
The concept of the Super Agent System is inherently multidisciplinary in nature. It combines elements of natural language processing, machine learning, cloud computing, edge computing, and user experience design. The successful implementation and deployment of such a system require close collaboration and expertise from these diverse fields.
In conclusion, the Super Agent System represents a significant step forward in making AI agents more powerful, efficient, and accessible. By accurately understanding user intent and leveraging appropriate tools, these agents can revolutionize the way we interact with technology. With continued advancements in technology, we can expect to see the seamless integration of super agents into our everyday lives in the near future.