Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day

2026-01-07

arXiv.org

AI Summary

This work addresses the critical barrier to reproducibility and integration with AI-for-Science (AI4S) agent workflows posed by the complex compilation and configuration of scientific software. We propose an end-to-end agent workflow that leverages a domain-specific taxonomy to filter repositories, automatically infers build specifications, containerizes the resulting artifacts, and validates their executability. For the first time, this pipeline enables the automated deployment of over 50,000 scientific tools within a single day. We successfully constructed reproducible execution environments for 50,112 tools, each verified by a minimal executable command, and released them in SciencePedia—the first trustworthy capability repository grounded in actual execution rather than documentation. Additionally, we publicly share the large-scale deployment traces to illuminate operational bottlenecks in scientific software packaging.

Technology Category

• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Open-source scientific software is abundant, yet most tools remain difficult to compile, configure, and reuse, sustaining a small-workshop mode of scientific computing. This deployment bottleneck limits reproducibility, large-scale evaluation, and the practical integration of scientific tools into modern AI-for-Science (AI4S) and agentic workflows. We present Deploy-Master, a one-stop agentic workflow for large-scale tool discovery, build specification inference, execution-based validation, and publication. Guided by a taxonomy spanning 90+ scientific and engineering domains, our discovery stage starts from a recall-oriented pool of over 500,000 public repositories and progressively filters it to 52,550 executable tool candidates under license- and quality-aware criteria. Deploy-Master transforms heterogeneous open-source repositories into runnable, containerized capabilities grounded in execution rather than documentation claims. In a single day, we performed 52,550 build attempts and constructed reproducible runtime environments for 50,112 scientific tools. Each successful tool is validated by a minimal executable command and registered in SciencePedia for search and reuse, enabling direct human use and optional agent-based invocation. Beyond delivering runnable tools, we report a deployment trace at the scale of 50,000 tools, characterizing throughput, cost profiles, failure surfaces, and specification uncertainty that become visible only at scale. These results explain why scientific software remains difficult to operationalize and motivate shared, observable execution substrates as a foundation for scalable AI4S and agentic science.

Problem

• scientific software deployment
• reproducibility
• AI-for-Science
• tool integration
• executable scientific tools

Innovation

• Deploy-Master
• AI-for-Science
• agentic workflows
• executable tool deployment
• containerized scientific software