The field of software development is undergoing a significant transformation with the increasing adoption of AI-powered tools and agents. These agents are capable of performing complex development tasks, including refactoring, and are changing the way developers work and interact with code. Recent studies have shown that agentic refactoring is a common and intentional activity, with agents targeting refactoring in a significant proportion of commits. The motivations behind agentic refactoring focus on internal quality concerns, such as maintainability and readability, and yield small but statistically significant improvements in structural metrics. Furthermore, research has explored the use of gaze-informed prompting to improve code comprehension and readability, and the creation of shared agentic memory architectures to enable collective continual learning among AI coding agents. Noteworthy papers in this area include: Agentic Refactoring, which presents a large-scale study of AI agent-generated refactorings in real-world open-source Java projects. GazeCopilot, which evaluates novel gaze-informed prompting for AI-supported code comprehension and readability. Smarter Together, which introduces a novel shared agentic memory architecture designed to emulate the collective intelligence and know-how of human developer communities.