We're not in the "AI as tooling" phase anymore. Your developers are working with agents that co-author PRs, copilots that suggest architecture decisions, and systems that know your codebase patterns better than your junior devs do. As engineering leaders, this changes everything. You're not just managing people - you're managing workflows where humans and AI collaborate, delegate, and sometimes disagree on the best approach.

At GitHub, I get to work with teams navigating this shift daily. The successful ones aren't just adopting AI tools faster. They're rethinking how work gets done, how decisions get made, and how trust gets built when your "team" includes non-human contributors.

What we'll cover:

- What "agentic engineering" actually looks like in practice (spoiler: it's messier than the demos)
- How to lead developers who are learning to work with AI, not just use it
- Building trust in AI systems without losing your humans in the process

You'll walk away with:

- Tips for hybrid human-AI workflows
- Practical strategies for scaling AI adoption without team burnout
- Tools for maintaining code quality and engineering culture as AI becomes a bigger contributor

This is happening in teams just like yours right now. Come ready for honest conversations about what works, what doesn't, and how to lead through the messiness.