Toward Super Agent System with Hybrid AI Routers

• URL: http://arxiv.org/abs/2504.10519v1
• Date: Fri, 11 Apr 2025 00:54:56 GMT
• Title: Toward Super Agent System with Hybrid AI Routers
• Authors: Yuhang Yao, Haixin Wang, Yibo Chen, Jiawen Wang, Min Chang Jordan Ren, Bosheng Ding, Salman Avestimehr, Chaoyang He,
• Abstract summary: Super agents can fulfill diverse user needs, such as summarization, coding, and research.<n>To make such an agent viable for real-world deployment and accessible at scale, significant optimizations are required.<n>This paper presents a design of the Super Agent System.
• Score: 19.22599167969104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach [35.05793485239977]
  We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents. We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet.
  arXiv  Detail & Related papers  (2025-03-20T00:48:44Z)
• AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation [27.984521240600493]
  Large language models (LLMs) have brought exciting new advances to mobile UI agents. One way to reduce the required model size is to customize a smaller domain-specific model. We propose to convert the UI task automation problem to a code generation problem.
  arXiv  Detail & Related papers  (2024-12-24T02:54:56Z)
• SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World [50.937342998351426]
  Chain-of-User-Thought (COUT) is a novel embodied reasoning paradigm. We introduce SmartAgent, an agent framework perceiving cyber environments and reasoning personalized requirements. Our work is the first to formulate the COUT process, serving as a preliminary attempt towards embodied personalized agent learning.
  arXiv  Detail & Related papers  (2024-12-10T12:40:35Z)
• CAMPHOR: Collaborative Agents for Multi-input Planning and High-Order Reasoning On Device [2.4100803794273005]
  We introduce an on-device Small Language Models (SLMs) framework designed to handle multiple user inputs and reason over personal context locally. CAMPHOR employs a hierarchical architecture where a high-order reasoning agent decomposes complex tasks and coordinates expert agents responsible for personal context retrieval, tool interaction, and dynamic plan generation. By implementing parameter sharing across agents and leveraging prompt compression, we significantly reduce model size, latency, and memory usage.
  arXiv  Detail & Related papers  (2024-10-12T07:28:10Z)
• xLAM: A Family of Large Action Models to Empower AI Agent Systems [111.5719694445345]
  We release xLAM, a series of large action models designed for AI agent tasks. xLAM consistently delivers exceptional performance across multiple agent ability benchmarks.
  arXiv  Detail & Related papers  (2024-09-05T03:22:22Z)
• Very Large-Scale Multi-Agent Simulation in AgentScope [112.98986800070581]
  We develop new features and components for AgentScope, a user-friendly multi-agent platform. We propose an actor-based distributed mechanism towards great scalability and high efficiency. We also provide a web-based interface for conveniently monitoring and managing a large number of agents.
  arXiv  Detail & Related papers  (2024-07-25T05:50:46Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only [21.054681757006385]
  We propose an agent that perceives its environment solely through screenshot images.<n>By leveraging the reasoning capability of the Large Language Models, we eliminate the need for large-scale human demonstration data.<n>Agent achieves an average success rate of 94.5% on MiniWoB++ and an average task score of 62.3 on WebShop.
  arXiv  Detail & Related papers  (2024-06-11T05:21:20Z)
• CACA Agent: Capability Collaboration based AI Agent [18.84686313298908]
  We propose CACA Agent (Capability Collaboration based AI Agent) using an open architecture inspired by service computing. CACA Agent integrates a set of collaborative capabilities to implement AI Agents, not only reducing the dependence on a single LLM. We present a demo to illustrate the operation and the application scenario extension of CACA Agent.
  arXiv  Detail & Related papers  (2024-03-22T11:42:47Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.