A HUB-ORCHESTRATED, ROLE-AWARE AGENTIC AI FRAMEWORK FOR COMMUNICATION AND COORDINATION IN HYBRID AGILE SOFTWARE TEAMS: A CONCEPTUAL FRAMEWORK

Abstract

Hybrid Agile teams integrates co-located and remote members working in the same development process. These teams often lose shared situational awareness, because information that would normally pass through informal, in-person conversation reaches team members inconsistently. Existing collaboration tools Jira, Slack, Microsoft Teams record this communication, but they do not interpret it. Team members are still left to judge, prioritize, and reason across roles on their own.

Current AI-based approaches do not fill this gap. Most target a single software-engineering task, such as defect prediction, code assistance, or meeting summarization.  LLM-based multi-agent systems go further, but they are built to produce software, not to manage team communication itself.

This paper proposes a Hub-Orchestrated, Role-Aware Agentic AI Framework to address this gap. The framework uses a layered architecture with four core steps. First, it collects multi-modal Agile communication from multiple sources. Second, it converts this communication into structured, traceable requirements. Third, six role-specific agents Product Owner, Developer, QA, UX/Design, DevOps, and Scrum Master analyze the requirements in parallel. Fourth, a central coordination engine resolves the agents’ outputs: it detects misalignment, resolves dependencies, and generates role-specific recommendations.

We describe this architecture in enough technical detail for independent implementation. We also illustrate its intended behavior through a conceptual application to three representative communication breakdowns, drawn from real-world Agile issue-tracking data recorded in the public Apache Jira data set.

The contribution of this paper is architectural and conceptual. We report no working implementation or empirical evaluation here. The illustrative cases demonstrate the framework’s applicability; they do not validate it. We close by outlining the prototype and evaluation plan needed to test the framework’s claims empirically.