Adding an LLM to a Flutter app is easy. Making that AI feature reliable enough to behave like part of a real product is much harder. A good prompt alone is rarely enough. The model needs the right application state, conversation history, user preferences, retrieved information and memory at the right moment. Once the model can use tools and take actions, we also need to control how it reasons, acts, observes results, recovers from failures and understands when to continue or stop. In this session, we'll build an AI-powered Flutter application and progressively evolve it from a simple LLM wrapper into a context-aware, tool-using agent. Along the way, we'll make the invisible parts of the system visible: what enters the model's context, what gets remembered, which tools it chooses, how the agent loop progresses, and what happens when something fails. We'll explore three engineering layers: prompts as behavioral contracts, context and memory as dynamically assembled application state, and agent loops as controlled state machines around a probabilistic model. We'll then add the pieces needed for production reliability: structured outputs, tool validation, error recovery and observable agent state in the Flutter UI. Finally, we'll connect the same ideas to AI-assisted development: why coding agents become more useful when we engineer their repository context, tools and validation loops instead of relying on increasingly elaborate prompts. Attendees will leave with a practical architecture and mental model for building AI features where the LLM is one component of the system, not the entire system.