TwinFlow: A Conceptual Framework for Digital Twin Personas in Agile Software Development

Abstract

Agile software development methodologies have revolutionized how teams collaborate and deliver software products. However, traditional project management approaches often struggle to capture the complex dynamics of team interactions and predict project outcomes effectively. This paper presents TwinFlow, a novel AI framework that leverages LLM-powered Multi-Agent Systems to create Digital Twin Personas (DTPs) that mirror individual team members in Agile workflows. Our approach integrates data from multiple development tools including Jira, GitHub, Slack, and Email to create autonomous agents that simulate collaboration patterns, predict task dynamics, and proactively resolve communication issues. Through persona-grounded inter-agent dialogue, TwinFlow presents a novel approach to Agile project management by proposing to capture the nuanced behavioral patterns of individual team members and their collaborative dynamics. This paper presents the system architecture, discusses implementation challenges, and identifies future research directions for empirical evaluation.