Development of Agentic Workflows with LangGraph for Software Development Life Cycle Automation

This paper aims to explore the development of agentic artificial intelligence workflows utilizing the open-source LangGraph framework. With the adoption of Large Language Models (LLMs) for various applications like text generation, summarization, image analysis, and code generation, many industries are looking for ways to utilize the power of these models to automate necessary tasks efficiently. LangGraph enables the creation of these multi-agentic workflows by providing an elaborative method for controlling agent interactions and execution flow. We present a methodology for software development lifecycle (SDLC) automation by leveraging LangGraph and discuss how agentic workflows are capable of enhancing efficiency and scalability through AI-driven automation.