The AppFunctions Jetpack framework allows apps to expose structured, callable capabilities directly to the OS and on-device AI agents. This marks a fundamental shift away from fragile screen scraping and rigid intents, enabling true agentic development where models natively discover and invoke your app's features. We will demystify how AppFunctions work and contrast them with older approaches like App Actions and MCP. Through a focused code demo, you will see how to define typed, callable functions that allow agents to execute tasks straight from natural language prompts. Finally, we will cover the practical realities: current limitations, backward compatibility, and essential security considerations. You will leave with a clear roadmap for preparing your app for the next generation of intelligent orchestration.

As AI-assisted and agentic development become standard, traditional engineering metrics are losing their meaning. Story points, PR counts, and velocity warp when teams use AI to generate code, tests, and documentation. While agentic tools unlock unprecedented capacity, they introduce a new risk: accelerating the delivery of low-value or low-quality work. If building software is no longer the bottleneck, how do we measure true engineering success? This roundtable explores how mobile engineering leaders must redefine metrics in an era of agentic development. We will debate whether traditional KPIs now reward the wrong behaviors and discuss shifting our focus from raw output to quality, developer experience, and customer impact. Topics for Discussion: The Metrics Mirage: Are story points, PR counts, and velocity obsolete, or just changing? Measuring AI Impact: How do we quantify the real ROI of AI-assisted development without rewarding output over user value? Quality & Guardrails: How do we prevent AI from accelerating poorly designed features or inflating our support burden? The New Signals: Should we be indexing purely on release confidence, lead time, and developer experience?