AI tools like GitHub Copilot and Claude are powerful out of the box -- but are you truly leveraging their full potential? Do you trust what the AI is suggesting, or are you constantly double-checking and tweaking its output? The difference between a helpful suggestion engine and a genuine AI collaborator that accelerates your development comes down to how well you've configured your environment.

In this session, we'll use GitHub Copilot to explore the customization layers that transform a generic code assistant into a partner that understands your architecture, follows your standards, and works autonomously on real tasks. You'll learn best practices for building specialized coding agents, extend capabilities with MCP servers and skills, and customize auditing and tracking — all tuned to your team's workflow. We'll also look at practical ways to craft agentic workflows to support Continuous AI. Walk away with practical techniques you can apply immediately to get dramatically better results from your AI tooling.

You will learn:

• How to create specialized coding agents tailored to your team's architecture and standards
• How and when to extend Copilot's capabilities using MCP servers, prompts, skills, and hooks
• Practical techniques for configuring AI tooling to deliver dramatically better results in your development environment