What happens after the perfect prompt? The agent may generate good code, but production Android development still requires product context, cross-layer contracts, device verification and production feedback. The problem is no longer writing a better prompt; it is engineering a reliable loop around the agent. At Blinkit, a feature rarely ends inside one Kotlin module. It can cross the Android client, server-driven UI, backend workflows, design tokens, analytics, tests and rollout configuration. The difficult part is not asking an agent to write a Composable. It is helping the agent discover everything that needs to change and proving that it has not missed anything. In this session, we’ll follow a feature through the development loop: from a requirement or Figma design to an agreed contract, implementation across layers, on-device verification and production feedback. We’ll cover: • Understanding context: What context means for AI-driven development, why source code alone is insufficient and how product and engineering knowledge changes the quality of the agent’s decisions. • Finding the right context: How structured knowledge about screens, flows, endpoints and existing components is combined with code search, semantic retrieval and graph traversal through MCP. • Turning a design into a contract: How we break down a design, find the closest existing implementation and verify the wire contract before generating code. • Building across layers: How the same workflow coordinates changes across the Android client, backend-for-frontend and server-driven UI instead of producing an isolated code snippet. • Closing the loop: How device previews, crash reports, logs, traces and Android vitals help diagnose production behavior, enrich the context layer and inform the next development cycle. This is not about an agent that magically understands the entire codebase. It is about reducing blind guesses, making review cycles smaller and giving the agent enough context to explain why each layer needs to change. By the end, you’ll have a practical blueprint for building AI-driven features that can work on real products at scale.