Google's official guidelines—Baseline Profiles, R8 Full Mode, JankStats, and Play Console dashboards—provide an essential foundation for Android performance. However, as applications scale across dozens of modules, bridging the gap between high-level dashboard metrics and line-by-line root causes requires deeper automated context. Manually analyzing 50MB Perfetto traces and raw benchmark JSON files for every PR can quickly create developer friction. In this session, we showcase how to build on top of Google's performance APIs by introducing an enterprise-grade, automated 5-step feedback loop: Observe → Diagnose → Fix → Validate → Guard. We will walk through how to build and connect custom performance tooling directly to AI Agents and CI pipelines: • AI-Driven Startup Diagnostics: Using an AI Agent + Perfetto MCP/Skills to automatically diff method-sampling traces, pinpointing screens which have main-thread lock contentions and Dagger verification bottlenecks across Cold, Warm, and Hot starts. • UI Jank & Memory Strategy: Pairing per-screen JankStats output with AI-driven heap dump analysis to isolate frozen frames (>700ms), pre-warm RecycledViewPools, and automate memory leak remediation. • Custom APK Size Analysis: Mapping compressed download bytes directly to source Gradle module boundaries to detect transitive SDK bloat, uncompressed assets, and the hidden "modularization tax" of DEX metadata. • Statistical Benchmark Validation: Using an interactive Macrobenchmarking report engine that evaluates 95% Confidence Intervals, Coefficient of Variation (CV) bounds, and run distributions to build real confidence in benchmark runs rather than only relying on standard mean/median & percentiles before code merges. Whether you are struggling with startup delays, scroll jank, or app bloat, you will leave with a practical blueprint to hook AI agents directly into your performance debugging workflows and CI/CD pipelines.