IMPLEMENTATION OF AI AGENTS FOR AUTOMATING TASK PRIORITIZATION IN PROJECT MANAGEMENT SYSTEMS
Keywords:
project management, intelligent agents, artificial intelligence, task prioritization, WSJF, RedmineAbstract
The article addresses the problem of automating task prioritization in software development
project management systems. Traditional prioritization approaches largely depend on subjective assessments made
by project managers and team members and do not always take into account the dynamic nature of the development
environment. In this regard, the use of intelligent agents and artificial intelligence methods for automating the task
prioritization process becomes increasingly relevant.
The purpose of the research is to develop and test an intelligent agent for the Redmine project management
system that automates task priority determination based on the Weighted Shortest Job First (WSJF) method. The
paper proposes an agent architecture that includes a data collection module using the project management system
API, an algorithmic processing module for calculating WSJF coefficients, an artificial intelligence module for risk
analysis and delay prediction, and a user interaction interface.
Within the scope of the study, a prototype plugin for Redmine was implemented, which uses custom fields
such as Business Value, Time Criticality, Risk Reduction, and Job Size to automatically calculate task priority
coefficients. Testing conducted on an experimental dataset demonstrated the correctness of the algorithm, the
convenience of integration with the Redmine interface, and the possibility of scaling the solution for projects with
a large number of tasks. The obtained results confirm that the use of intelligent agents can improve the objectivity
and efficiency of backlog management, reduce the effort required for manual prioritization, and ensure
transparency in decision-making.
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