Agent-Based Software Deployment Framework
DOI:
https://doi.org/10.15662/IJEETR.2026.0802062Keywords:
Multi-Agent AI, Automated Software Setup, Cross-Platform Installation, Dependency Resolution, Self- Healing Systems, LLM-Assisted AutomationAbstract
Individuals across academic, industrial, and training environments—including non-computer science learners, technology newcomers, educators, trainees, and professionals—often face difficulties installing and configuring development tools. Manual setup requires version selection, dependency resolution, environment configuration, and error handling, making it time-consuming and error-prone for users with limited technical expertise. This paper presents a Agent - Based Software Deployment Framework, an intelligent framework designed to automate the installation and configuration of development environments while addressing the limitations of existing setup tools. The proposed system employs multiple autonomous agents to collaboratively plan, execute, monitor, and recover software installations in real time. It supports Windows, Linux, and macOS platforms, handling diverse installer formats including .exe, .msi, .deb, and .dmg, as well as native package managers. The Planner Agent determines a dependency-aware installation sequence, the Installer Agent performs silent and unattended installations, the Monitor Agent verifies execution outcomes, and the Recovery Agent enables self-healing through retries, alternative installation strategies, and context-aware user prompts when necessary. To overcome the limitations of deterministic rule- based automation, the system integrates an LLM-based reasoning module for intelligent failure diagnosis and decision- making, thereby improving robustness and installation success rates. Additionally, scheduler-based automation and voice- command interfaces facilitate seamless initiation of installations, while a graphical user interface provides real-time logs, progress indicators, and environment validation. Experimental results show that the proposed system reliably provisions development environments across platforms, achieving an installation success rate of 96%, a dependency verification accuracy of 98%, a scheduling accuracy of 99%, and a download completion rate of 99%. The framework autonomously resolves dependencies, handles failures intelligently, and minimizes setup time and user intervention, providing a scalable and user-friendly solution for diverse technical user groups
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