The field of software engineering is witnessing significant developments in the adoption of agile practices and the integration of machine learning (ML) sustainability. Researchers are exploring the impact of agile practices on employee performance, highlighting the need for comprehensive studies in diverse contexts. Meanwhile, the sustainability of ML-enabled systems is becoming a key concern, with a growing recognition of the need for structured guidelines and measurement frameworks to support environmentally, socially, and economically responsible ML development. Studies are also investigating the challenges of implementing ML in the public sector, where data governance, human validation, and institutional engineering are crucial for building trustworthy and operationally sustainable ML systems. Furthermore, researchers are examining the constraints and opportunities for adopting continuous software engineering (CSE) in complex organizations, emphasizing the importance of internal improvements and realistic adoption goals. Noteworthy papers in this area include:

• Employee Performance when Implementing Agile Practices in an IT Workforce, which explores the influence of agile practices on employee performance in South African IT workforces.
• Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner's Perspective, which characterizes sustainability in ML-enabled systems from a practitioner's perspective and highlights the need for more structured guidelines and regulatory support.