Abstract
Abstract Modern agile development and time-to-market pressures often lead to significant hidden technical debt (TD), which must be managed for sustainable software delivery. The growing use of artificial intelligence (AI) for code generation can further increase TD if applied improperly. At the same time, software processes are becoming increasingly data-driven. This creates an opportunity: using AI to enable data-driven TD management in agile environments. This workshop offers a platform for researchers and practitioners to share ideas, experiences, and future visions on applying AI for TD management in agile development. The workshop was held in a half day and it consisted of three phases informing each other: (1) scientific talks (i.e., two paper presentations and two keynotes) that framed current challenges and their relevance; (2) two industrial talks shaping these problems in practice; and interactive sessions (i.e., group discussions and a fish bowl panel) that allowed participants to reflect about the current status of AI with respect to TD and next steps. These discussions define a research agenda that spans measurement, practices, education, and legacy modernization, and call for tight collaboration between industry and academia to keep pace with AI-augmented software development.