Every developer now has a menu of AI coding tools, and every vendor says theirs is the one you need. GitHub Copilot lives inside the IDE and the repo you already use. Claude and Codex arrived as terminal-first agents and grew into cloud agents, SDKs, and IDE integrations of their own. Gemini and a growing field of others want a seat at the table too. Meanwhile the models behind all of them keep leapfrogging each other every few months. Picking a tool by feature checklist is a losing game, because the checklist will be wrong by the time you finish reading it.

In this demo-driven session, Brian puts the major contenders side by side on the same tasks: completion and chat in the editor, multi-file agent work, cloud agents that take an issue and hand back a pull request, and the governance and billing questions that matter once a whole team is involved. You’ll see where each tool shines, where each one stumbles, and how to tell the difference between a real capability gap and a marketing one. Whether you are choosing for yourself, standardizing for a team, or trying to justify running more than one, you’ll leave with a decision framework that outlasts the next release cycle.

You will learn:

• A practical framework for comparing AI coding tools on the dimensions that persist: workflow fit, agent autonomy, model choice, governance, and cost
• Where GitHub Copilot, Claude, Codex, and other contenders differ in practice, with live demos of the same tasks across each
• How to choose, standardize, or combine tools for a .NET and Azure team without betting on a single vendor