Frequently Asked Questions
What is the focus of the track?
The focus is on the evolution of engineering leadership and team dynamics during AI adoption. It addresses how to rethink team structures, maintain ownership, and preserve engineering discipline when traditional coordination and quality-control methods are challenged by the speed and nature of AI systems.
How do we handle "blurred ownership" when AI agents act across different team boundaries?
The track explores Team Structures for Modern Development, focusing on organizing around system boundaries. You’ll learn how to redefine ownership and coordination so that even when agents are involved, the responsibility for system integrity remains clear and human-led.
What does "AI Literacy" mean for a software engineer in this context?
It’s not just about knowing how to write prompts. It’s about understanding the underlying behavior of AI systems so engineers can identify unreliable outputs and architectural risks. The track emphasizes teaching engineers what to trust and, crucially, what to stay skeptical about.
How can we maintain our engineering culture if AI is doing the heavy lifting?
A major topic is Engineering Culture & Discipline. You’ll learn how to prevent "architectural erosion"—where generation replaces deliberation. The track provides a framework for keeping human judgment and quality at the center of the development process, even under constant delivery pressure.
What kind of governance is required for AI-augmented systems?
You will explore Governance & Responsible Technology Use, focusing on the policies and accountability models needed for AI systems. This includes building guardrails that ensure AI-generated results align with organizational standards and that there is a clear path for human intervention when systems deviate.
