Building a Proper Backend for My LangGraph AI Agent

An AI agent built with LangGraph is upgraded from a demo with in-memory storage to a more robust application with a persistent backend. The initial in-memory approach is insufficient for a real product because conversation states and bookings are lost on restart and cannot be shared between sessions. To solve this, a PostgreSQL database is integrated to store booking data and manage conversation checkpoints. The architecture uses a repository protocol to abstract the database interaction, allowing the system to use either Postgres for production or an in-memory version for testing. This change enables the agent's graph nodes to reliably read and write booking data, turning the demo into a functional product.