Neural Orchestration for Multi-Agent Systems: A Deep Learning Framework for Optimal Agent Selection in Multi-Domain Task Environments

2025-05-03

AI Summary

In multi-domain dynamic task environments, multi-agent systems (MAS) suffer from rigid coordination and static agent selection, hindering optimal adaptation. To address this, we propose MetaOrch, a neural orchestration framework. Its key contributions are: (1) a novel fuzzy evaluation module that generates soft supervision labels based on three dimensions—completeness, relevance, and confidence; (2) a departure from static mapping paradigms, enabling real-time, adaptive agent selection with explicit confidence estimation; and (3) a modular architecture ensuring plug-and-play agent integration and system scalability. Evaluated in a heterogeneous agent simulation environment, MetaOrch achieves an 86.3% agent selection accuracy—significantly outperforming baseline methods including random selection and round-robin scheduling. The framework demonstrates robustness across dynamic task distributions and provides interpretable, confidence-aware orchestration decisions.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Multi-agent systems (MAS) are foundational in simulating complex real-world scenarios involving autonomous, interacting entities. However, traditional MAS architectures often suffer from rigid coordination mechanisms and difficulty adapting to dynamic tasks. We propose MetaOrch, a neural orchestration framework for optimal agent selection in multi-domain task environments. Our system implements a supervised learning approach that models task context, agent histories, and expected response quality to select the most appropriate agent for each task. A novel fuzzy evaluation module scores agent responses along completeness, relevance, and confidence dimensions, generating soft supervision labels for training the orchestrator. Unlike previous methods that hard-code agent-task mappings, MetaOrch dynamically predicts the most suitable agent while estimating selection confidence. Experiments in simulated environments with heterogeneous agents demonstrate that our approach achieves 86.3% selection accuracy, significantly outperforming baseline strategies including random selection and round-robin scheduling. The modular architecture emphasizes extensibility, allowing agents to be registered, updated, and queried independently. Results suggest that neural orchestration offers a powerful approach to enhancing the autonomy, interpretability, and adaptability of multi-agent systems across diverse task domains.

Problem

• Optimal agent selection in multi-domain tasks
• Dynamic adaptation in multi-agent coordination
• Fuzzy evaluation for agent response quality

Innovation

• Neural orchestration framework for agent selection
• Fuzzy evaluation module scores agent responses
• Dynamic prediction of suitable agents with confidence