AI Agents in Software Engineering Optimizing Software Development Processes and Enhancing Security Management in Learning Management Systems

Abstract

The use of AI agents in software engineering is an area of research that offers remarkable possibilities to improve software development processes and security?management in LMS. In this?paper, we investigate the use of AI agents in LMS development, concerning the AI ability to automate software engineering tasks, enhance system performance and guarantee secure security measures. AI-based resources such as machine learning algorithms, natural language processing, and prescriptive analytics?can improve much of the software lifecycle management process from requirements gathering to deployment with automating some of the most difficult hurdles such as determining system updates in real time, and understanding what failures can happen. In addition, AI agents can greatly improve?security management through their ability to recognize vulnerabilities, automate threat scanning, and proactively guard against potential threats. The paper provides an overview of AI applications in software engineering?and a framework to use AI agents in LMS environments as means to cut down manual work, speed up development, and increase system security. The paper concludes by suggesting future research directions in AI-enhanced software engineering, emphasizing on how AI\'s evolving role can?help to meet growing cybersecurity challenges in educational technologies.

Introduction

The study explores the integration of Artificial Intelligence (AI) agents into software engineering and security management for Learning Management Systems (LMS). AI agents are employed to automate software development tasks—including code generation, bug detection, and optimization—using machine learning and NLP, which reduces repetitive work, shortens development cycles, and improves code quality.

For security management, AI agents monitor system activity, detect anomalies, and respond to threats in real time using methods like anomaly detection, Gaussian Mixture Models, and deep learning, providing robust protection against cyberattacks and safeguarding sensitive educational data.

Additionally, AI enhances adaptive learning in LMS through reinforcement learning, dynamically adjusting course content and feedback to improve student engagement and performance.

The proposed hybrid architecture consists of:

1. Software Development Agent (SDA) – automates code-related tasks.

2. Security Management Agent (SMA) – monitors, detects, and responds to threats.

3. Data Pipeline – collects and processes real-time and historical data for AI training.

4. Learning Layer – applies reinforcement learning for adaptive education.

Evaluation shows that integrating AI agents in LMS leads to reduced bugs, faster development, improved security threat detection, and enhanced student outcomes, demonstrating AI’s potential to optimize software development, security, and personalized learning simultaneously.

Conclusion

In this paper, we have examined the incorporation of AI agents in the software development life cycle and as well as of security management in Learning Management?Systems (LMS). \"This has proved to us just how powerful AI can be in the optimization of software development, or in enhancing security?measures, and of course in improving student learning outcomes. Through use of machine learning algorithms, natural language processing and deep learning, AI agents were able to significantly, reducing bugs helping speed up development?cycles, identifying anomalies and improving identification of threats. In addition, I think the reinforcement learning-powered adaptive learning system made an impressive improvement of student\'s performance, thus advocating a more?personal and delightful learning journey.

References

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Copyright

Copyright © 2025 Rajendra Varma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.