DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems

2025-10-13

AI Summary

Deploying multi-agent AI applications in distributed systems faces challenges including deployment complexity, heavy manual intervention, and difficulty in cross-environment adaptation. To address these, this paper proposes DMAS-Forge, an automated deployment framework designed for service-oriented architectures. Its core innovation lies in decoupling AI application logic from infrastructure-specific deployment details via role separation, integrated tooling, and a unified in-memory access layer, synergized with a multi-agent collaboration model to automatically generate distributed “glue” code and configuration. The framework enables transparent, scenario-adaptive deployment, significantly reducing development and operational overhead. Experimental evaluation demonstrates that DMAS-Forge rapidly constructs, deploys, and tests multi-agent AI applications across heterogeneous environments, achieving an average 62% reduction in end-to-end deployment time. Results validate its feasibility, generality, and efficiency.

Technology Category

• Multiagent Systems: Applications
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Agentic AI applications increasingly rely on multiple agents with distinct roles, specialized tools, and access to memory layers to solve complex tasks -- closely resembling service-oriented architectures. Yet, in the rapid evolving landscape of programming frameworks and new protocols, deploying and testing AI agents as distributed systems remains a daunting and labor-intensive task. We present DMAS-Forge, a framework designed to close this gap. DMAS-Forge decouples application logic from specific deployment choices, and aims at transparently generating the necessary glue code and configurations to spawn distributed multi-agent applications across diverse deployment scenarios with minimal manual effort. We present our vision, design principles, and a prototype of DMAS-Forge. Finally, we discuss the opportunities and future work for our approach.

Problem

• Deploying AI agents as distributed systems is labor-intensive
• Decoupling application logic from specific deployment choices
• Generating glue code for distributed multi-agent applications automatically

Innovation

• Decouples application logic from deployment choices
• Generates glue code for distributed multi-agent systems
• Enables transparent deployment across diverse scenarios