Shift Handover Pro

Software maintenance becomes difficult when a development request is left partially implemented. The next engineer must reconstruct what was requested, understand what has already changed, identify what remains incomplete, and determine which parts of the application could be affected. This manual re-orientation can slow maintenance and increase the risk of changing existing behavior unintentionally. Shift Handover Pro is an AI-assisted maintenance resume tool designed to help engineers safely continue unfinished development work. Our demonstration uses an ATC (air traffic control) shift-handover application with a pending equipment-status change. The requested change was to simplify a legacy fault equipment state into three states: Operational, Maintenance, and Offline. Instead of treating the change as an isolated code edit, Shift Handover Pro demonstrates how IBM Bob 2.0 Agent mode can work with full-repository context to understand the application and identify the areas affected by the request. Bob identified four key touch points before implementation: the CSS selector, Supabase migration, dashboard logic, and handover form UI. The change was then implemented with backward compatibility in mind, including handling legacy status data without requiring manual database work. The target users are software developers, maintenance engineers, technical teams, and organizations working with existing or legacy applications where development tasks may be paused, transferred, or resumed by another engineer. Users interact with the workflow by providing or continuing a maintenance request, reviewing the repository-aware analysis, examining the affected components, and validating the resulting application behavior. The live demonstration shows the updated equipment-status workflow and the corresponding IBM Bob development evidence. What makes Shift Handover Pro different is its focus on context recovery rather than simple code generation. The goal is not merely to ask AI to write code, but to understand the relationship between an unfinished request and the existing repository before making a change. The workflow is: Change Request → Full Repository Context → Impact Analysis → Planning → Implementation → Verification This approach demonstrates how repository-aware AI can reduce repetitive re-orientation during maintenance while keeping the requested change connected to the existing application behavior.