# AI and Agile Software Development: From Frustration to Success -- XP2025 Workshop Summary

arXiv: https://arxiv.org/abs/2506.20159 · HTML: https://mlsanity.com/paper/2506.20159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [LLM-based agents for automating the enhancement of user story quality: An early report](https://mlsanity.com/paper/2403.09442/) (2024)
- [Exploring Human-AI Collaboration in Agile: Customised LLM Meeting Assistants](https://mlsanity.com/paper/2404.14871/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI and Agile Software Development: A Research Roadmap from the XP2025 Workshop](https://mlsanity.com/paper/2508.20563/) (2025)

## Abstract (arXiv)

The full-day workshop on AI and Agile at XP 2025 convened a diverse group of researchers and industry practitioners to address the practical challenges and opportunities of integrating Artificial Intelligence into Agile software development. Through interactive sessions, participants identified shared frustrations related to integrating AI into Agile Software Development practices, including challenges with tooling, governance, data quality, and critical skill gaps. These challenges were systematically prioritized and analyzed to uncover root causes. The workshop culminated in the collaborative development of a research roadmap that pinpoints actionable directions for future work, including both immediate solutions and ambitious long-term goals. The key outcome is a structured agenda designed to foster joint industry-academic efforts to move from identified frustrations to successful implementation.