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

2026-08-02

AI Summary

This work addresses the challenges of automating the deployment of open-source AI models as callable APIs, a process often hindered by complexity and prolonged timelines. To overcome the inefficiencies of existing approaches, the authors propose MADE, a novel system featuring a belief-driven dual-agent collaborative architecture that iteratively constructs, validates, and retrospectively refines deployment artifacts through execution feedback, enabling end-to-end autonomous deployment. Key contributions include a large language model–based mechanism for dual-agent coordination and belief updating, an automated pipeline for code generation and test validation, a method for parsing heterogeneous model resources, and M2ABench—the first benchmark for model-to-API conversion. Evaluated on M2ABench, which comprises 122 real-world models, MADE achieves a deployment success rate of 68.85%, substantially outperforming SWE-agent and OpenHands.

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.

Problem

• automated model deployment
• LLM-based agents
• tool integration
• API serving
• heterogeneous models

Innovation

• dual-agent coordination
• belief-driven deployment
• automated model deployment
• LLM-based agents
• M2ABench