COMMON POWERUp 2026

The use of agentic AI (AI tools with agency to execute tasks) is rising. Organizations are adopting this technology, including some of our friends in the IBM i space! This is exciting new technology, but its security implications are unclear. In this session we will focus on the safe configuration of an example agentic AI application on the IBM i and discuss the security considerations around this technology to ensure agents aren’t overauthorized, activities can be tracked, and your data remains secure.

Learning Objectives:

Learn about: 1. Open source and agentic AI technologies on IBM i 2. Security implications of these technologies 3. What these transactions "look like" through traditional tools like the audit journal

Git has become the standard for version control, but moving traditional IBM i source code into Git requires planning, the right tools, and some trial and error. This session walks through a complete migration experience: from the initial planning phase, through the technical steps of exporting source, to resolving issues with members, naming, syncing, and how AI can help. Along the way, we’ll discuss pitfalls, solutions, and strategies for adoption across development teams. Attendees will gain a clear roadmap for migrating to Git and the confidence to start their own journey.

Learning Objectives:

Identify key planning steps needed before migrating IBM i source code into Git, including repository structure, workflow design, and tool selection. Execute a practical migration process — from exporting IBM i source members to resolving naming and synchronization challenges. Recognize common pitfalls during migration (e.g., member-level management, library mapping, and CCSID issues) and apply effective solutions. Leverage AI-assisted tools to streamline source analysis, refactoring, and ongoing Git synchronization. Develop a strategy for team adoption, including training, workflow standardization, and integration with CI/CD or modern IDEs. Build confidence to begin their own Git journey, using a clear roadmap and lessons learned from real-world implementation.

The Rest API engine for IBM i continues to improve to meet the needs of our development community. In this session we will review the recent change to support Jakarta EE, what does that mean and how to take advantage of these new features. We will include live demo where ever possible to help you understand how easy it is to use and how powerful this integrated features you already own is.

Learning Objectives:

Learn about the integrate Rest API engine and how to use

Podman (and its peer, Docker) provide a powerful way to leverage containers for everything from development to full-scale production. In this session, we will explore the unique value of containerization and cover some of the basic features of these tools.

Learning Objectives:

Understand the value of Linux container tooling and gain a basic understanding of how to use them.

We've all heard that AI will revolutionize the way we do business, but how can we connect that AI inspired future technology to our present day systems of record hosted on IBM i and Db2 for i. In this session we will to demonstrate to an intermediate level audience on how to solve this business problem. Linking AI technology with our IBM i backend systems, securely and in real time. With practical tips and a working proof of concept delivered within the session, attendees will be better informed and ready to build their own connected watsonx Assistants.

Learning Objectives:

1. Learn approaches to connecting IBM watson X and watsonx orchestrate to Db2 for i data, securely. 2. Learn from our experience on what works and what doesn't work when connecting AI and Db2 for i. 3. Resources to get you started with building your own IBM watsonx assistanct consuming IBM i Db2 for i data.

While the capabilities of artificial intelligence will drive plenty of new business value, it's important that moral principles are still applied. In this guided (but open) discussion, we will talk about several ethical points to consider, soanning the obvious and hidden.

Learning Objectives:

Ethics in AI