In the rush to integrate AI into the software development lifecycle, the industry has overlooked a fundamental truth of data science: A model is only as deterministic as the features that feed it. For mobile engineering teams, the "Action Gap”, the distance between detecting a crash and actually resolving it, remains wide. This is because traditional observability relies on "shallow signals": fragmented, low-fidelity data that forces LLMs to guess rather than act. In this session, we move beyond basic monitoring to explore why AI-driven engineering requires High-Fidelity Context. We’ll examine how capturing deep, mobile-first data at the source is the only way to power truly autonomous workflows. By shifting from 'shallow signals' to multi-dimensional telemetry, mobile teams can finally move from reactive firefighting to a future of deterministic, automated resolution. Attendees will learn: -The Dimensionality of Disaster: Why a stack trace is only a 1D "ghost" of a 3D problem, and how sequential visual data provides the "Ground Truth" needed for autonomous reasoning. -The SDK as a Feature Store: How to reposition the mobile SDK from a "recorder" to a high-density encoder that feeds "Inference-Ready" features to Agentic workflows. -Closing the Action Gap: A blueprint for moving from Probabilistic guesswork to a Reliable resolution by grounding agents (like Claude Code) in the objective reality of the mobile state. The Conclusion: This is the roadmap for the next era of mobile engineering: where Context isn’t just a "nice-to-have" metadata point, but the strategic "Queen" that actually runs the board.