Next-Generation Software Engineering: A Multi-Agent System for End-to-End Sdlc Automation Using Large Language Models

DOI

10.2991/978-94-6239-616-6_5How to use a DOI?

Keywords

Software Development Life Cycle (SDLC); Generative AI; Multi-Agent Systems; Large Language Models (LLMs); Automation; Human-in-the-loop; Adaptive Learning; LangGraph

Abstract

Software development, a multi-phase process composed of requirements, coding, testing, and deployment that can take a lot of time, be error-prone, and consume a lot of resources to do manually. AI-assisted tools that currently exist, such as code suggestion tools, are focused on single tasks and do not provide an end-to-end automation of the Software Development Life Cycle. The purpose of the paper is to introduce an intelligent solution that will utilize Generative Artificial Intelligence (AI) and Multi-Agent Systems to automate the SDLC processes. The intelligent system will include all the phases of SDLC using specialized agents including a Requirements Agent, a Code Generation Agent, a Test Agent, and a Fixer Agent, all orchestrated by a Controller Agent that will manage all the collaborative workflows. The agents will generate context-aware outputs by leveraging Large Language Models (LLM) with a human-in-the-loop to validate their results and ensure rigorous quality control. Additionally, the approach will use adaptive learning loops to continuously improve based on test results, bug fixes, and software developer feedback. The intelligent solution represents a scalable and cost-effective approach to accelerate software delivery while maintaining low-quality and high-reliability development. The proposed solution will be a significant step towards a new paradigm of software development by leveraging AI to expand software engineering leading to a revolution in accelerating how users and organizations develop software and ultimately designers’ ability to build software applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - T. Periyasamy
AU  - J. Anburaj
AU  - K. Fyzal
AU  - B. Jayakrishnan
AU  - S. Prasanth
PY  - 2026
DA  - 2026/03/31
TI  - Next-Generation Software Engineering: A Multi-Agent System for End-to-End Sdlc Automation Using Large Language Models
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 53
EP  - 65
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_5
DO  - 10.2991/978-94-6239-616-6_5
ID  - Periyasamy2026
ER  -