Skip to content
Log in
Preprint

MADE: Belief-Driven Dual-Agent Coordination for Autonomous Model Deployment

Yicheng Liu Bolin Zhang Weiran Liu Yakun Zhang Yangqin Jiang Zhiying Tu Dianhui Chu
Aug 2026 · 0 citations · 28 references
Computer Science

TL;DR

This work introduces Model Automated Deployment Engine (MADE), a dual-agent coordination system that iteratively constructs and validates the deployment artifacts, updates its deployment belief based on execution feedback, and revisits invalid upstream artifacts until the model is successfully served as a ready-to-call API that can then be used by other agents.

Abstract

LLM-based agents now have strong general capabilities. However, they still struggle with domain-specific tasks, motivating the integration of external tools to broaden their capabilities. The open-source community offers a vast array of AI models typically released as heterogeneous research artifacts, whereas transforming them into ready-to-call APIs is costly and labor-intensive. Automated model deployment is therefore essential for bridging the gap between model resources and tool usability, yet it remains a long-horizon, multi-stage task that has not been sufficiently explored. To tackle this challenge, we introduce Model Automated Deployment Engine (MADE), a dual-agent coordination system. Specifically, given a model resource, MADE iteratively constructs and validates the deployment artifacts, updates its deployment belief based on execution feedback, and revisits invalid upstream artifacts until the model is successfully served as a ready-to-call API that can then be used by other agents. We further introduce M2ABench, a benchmark for the task of transforming Models to ready-to-call APIs. M2ABench comprises 122 real-world models with standardized test cases for evaluation. Experimental results demonstrate that MADE achieves a deployment success rate of 68.85%, outperforming SWE-agent and OpenHands by 13.93 and 44.26 percentage points, respectively. Our code and dataset are publicly available at https://github.com/HITDiSC/MADE.

arXiv Semantic Scholar
View source

Similar papers

Conference Jul 2026

An Expert-Guided Architecture for Modular and Multi-Client Multi-Agent Systems

Large Language Model (LLM) applications increasingly rely on multi-agent and retrieval-augmented generation (RAG) architectures to solve complex, knowledge-intensive tasks. However, when deployed as generic platforms serving multiple customers and heterogeneous user groups, existing systems often rely on agent-level routing and prompt hardcoding, leading to poor modularity, limited reuse, and weak controllability. In particular, current approaches lack an explicit abstraction for modeling system capabilities and controlling which capabilities are accessible to different users. We propose an expert-guided multi-agent architecture that separates execution from capability modeling. Agents are responsible for LLM interaction and tool execution, while experts represent indivisible business or task capabilities and guide agent orchestration through dynamic prompt injection. This design enables a generic, multi-instance system in which different customer deployments and user groups share the same agent implementations while exposing different capability sets at the business level. The architecture enforces strong guarantees in terms of answerability, traceability, and controllability. A first working implementation has been developed, and an open-source release is currently in preparation.

Ying Zhang, M. Guillaume, Edouard Leleux et al. · 0 citations
Review Open access Aug 2026

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.

Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al. · 0 citations
Preprint Jul 2026

AgentRadio: Passive Awareness for Long-Horizon Multi-Agent Collaboration

AgentRadio is presented, an asynchronous message-passing layer that equips coding-agent harnesses with three primitives: threads, messages, and waiting for mentions that shows the gain growing with task difficulty, consistent with mid-course correction as the underlying mechanism.

Xinxing Ren, Qianbo Zang, Ziyan Wang et al. · 0 citations
Book Open access Jul 2026

Agents in the Wild: Where Research Meets Deployment

Through applied case studies in pharmaceutical discovery and financial systems, common design patterns that make agentic systems successful are analyzed, and practical mitigation strategies for failure modes are discussed, such as verification pipelines, fallback mechanisms, and human-in-the-loop supervision.

Grace Hui Yang, P. Venkit, Hooman Sedghamiz et al. · 0 citations
Preprint Aug 2026

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback

Agent Gym is introduced, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop and introduces the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency.

Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge et al. · 0 citations