Autonomous agents powered by Large Language Models are transforming AI, creating an imperative for the visualization area. However, our field's focus on a human in the sensemaking loop raises critical questions about autonomy, delegation, and coordination for such agentic visualization that preserve human agency while amplifying analytical capabilities. This paper addresses these questions by reinterpreting existing visualization systems with semi-automated or fully automatic AI components through an agentic lens. Based on this analysis, we extract a collection of design patterns for agentic visualization, including agentic roles, communication, and coordination. These patterns provide a foundation for future agentic visualization systems that effectively harness AI agents while maintaining human insight and control.

Native game engines have long been the 3-D development platform of choice for research in mixed and augmented reality. For this reason, they have also been adopted in many immersive visualization and immersive analytics systems and toolkits. However, with the rapid improvements of WebXR and related open technologies, this choice may not always be optimal for future visualization research. In this article, we investigate common assumptions about native game engines versus WebXR and find that while native engines still have an advantage in many areas, WebXR is rapidly catching up and is superior for many immersive analytics applications.

In this article, we share our reflections on visualization literacy and how it might be better developed in early education. We base this on lessons we learned while studying how teachers instruct, and how members acquire basic visualization principles and skills in elementary school. We use these findings to propose directions for future research on visualization literacy.

Visualization is coming of age: with visual depictions being seamlessly integrated into documents and data visualization techniques being used to understand datasets that are ever-growing in size and complexity, the term visualization is becoming used in everyday conversations. But we are on a cusp; visualization researchers need to develop and adapt to today's new devices and tomorrows technology. Today, we are interacting with visual depictions through a mouse. Tomorrow, we will be touching, swiping, grasping, feeling, hearing, smelling and even tasting our data. The next big thing is multi-sensory visualization that goes beyond the desktop.

There is a growing demand in engineering, business, science, research, and industry for members with visual analytics expertise, but teaching visual analytics is challenging due to the multidisciplinary nature of the topic matter, the diverse backgrounds of the members, and the corresponding requirements on the instructor. We report some best practices from our experience teaching several offerings of a visual analytics graduate course at Purdue University where we leveraged these multidisciplinary challenges to our advantage instead of attempting to mitigate them.