This second session in this 4-part series moves from understanding GitHub Copilot CLI to using it in real development work.

Join us for an inspiring panel featuring leaders from some of the most impactful STEM workforce development organizations, including programs like Year Up and MSSA (Microsoft Software and Systems Academy).

In part 3 of this 4-part series, you'll learn how to use GitHub Copilot CLI to automate repeatable work by using skills - natural language descriptions of workflows and processes.

In today’s rapid transformation environment, organizations often discover that successful change is not just about delivering new tools or processes - it is about driving real adoption at the individual level.

As AI agents move into enterprise workflows, they introduce a new class of identity—autonomous, highly privileged, and often ungoverned.

In this fourth and final session, you'll learn how to use Model Context Protocol (MCP) servers to connect GitHub Copilot CLI to external systems so it can work with GitHub, local files, documentation, databases, APIs, and more from the terminal.

Agile transformations often struggle not because teams fail to improve, but because organizations never clearly define what success looks like or how progress should be understood.

What does it actually take to move AI from experimentation to everyday impact?

In today’s environment, innovation and AI are accelerating change faster than clarity can keep up. Leaders are expected to make decisions, communicate direction, and move work forward— even when information is incomplete and the path ahead isn’t fully defined.

We all encounter difficult people in the workplace. We may be that way ourselves from time to time.

Did you know that two individuals can send up to 800 nonverbal cues in the span of 30 minutes? That with up to 70% accuracy, people can predict the outcome of an election, interview, etc. based on pictures of the individuals body language?