AgentOrchestra: A Hierarchical Multi-Agent Framework for General-Purpose Task Solving

2025-06-14

AI Summary

Existing LLM-based agent systems lack effective coordination mechanisms among specialized sub-agents and exhibit limited cross-domain generalization in complex tasks. Method: We propose a hierarchical multi-agent framework featuring a central planning agent that dynamically decomposes tasks and orchestrates domain-specialized sub-agents (e.g., data processing, web interaction, multimodal reasoning). Inspired by orchestral conducting, we introduce a novel coordination mechanism grounded in four principles: scalability, multimodality, modularity, and strong collaboration—enabling explicit sub-goal modeling, structured inter-agent communication, and role-adaptive assignment. The framework integrates a program analysis toolchain, multimodal perception interfaces, and a dynamic web navigation module. Results: Evaluated on three real-world task benchmarks, our framework achieves state-of-the-art success rates and cross-domain adaptability, significantly outperforming flat and monolithic baselines.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advances in agent systems based on large language models (LLMs) have demonstrated strong capabilities in solving complex tasks. However, most current methods lack mechanisms for coordinating specialized agents and have limited ability to generalize to new or diverse domains. We introduce projectname, a hierarchical multi-agent framework for general-purpose task solving that integrates high-level planning with modular agent collaboration. Inspired by the way a conductor orchestrates a symphony and guided by the principles of extit{extensibility}, extit{multimodality}, extit{modularity}, and extit{coordination}, projectname features a central planning agent that decomposes complex objectives and delegates sub-tasks to a team of specialized agents. Each sub-agent is equipped with general programming and analytical tools, as well as abilities to tackle a wide range of real-world specific tasks, including data analysis, file operations, web navigation, and interactive reasoning in dynamic multimodal environments. projectname supports flexible orchestration through explicit sub-goal formulation, inter-agent communication, and adaptive role allocation. We evaluate the framework on three widely used benchmark datasets covering various real-world tasks, searching web pages, reasoning over heterogeneous modalities, etc. Experimental results demonstrate that projectname consistently outperforms flat-agent and monolithic baselines in task success rate and adaptability. These findings highlight the effectiveness of hierarchical organization and role specialization in building scalable and general-purpose LLM-based agent systems.

Problem

• Lack of coordination mechanisms for specialized agents in current LLM systems
• Limited generalization ability to new or diverse domains in existing methods
• Need for scalable hierarchical frameworks integrating planning and modular collaboration

Innovation

• Hierarchical multi-agent framework for task solving
• Central planning agent delegates specialized sub-tasks
• Supports multimodal, modular, and adaptive coordination