Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 22·5 min read
Building a Proper Backend for My LangGraph AI Agent
TL;DR
A robust backend was developed for the LangGraph AI agent, enabling it to handle real booking data effectively. This transformation enhances the agent's functionality and reliability in practical applications.
✦ Why It Matters
Implement a robust backend for your AI projects to enhance data handling and operational reliability.
Key Takeaways
- Transition from in-memory to Postgres for data persistence.
- Implement a protocol for flexible data storage options.
- Prevent double bookings by sharing availability across sessions.
- Utilize the provided GitHub repository for testing and development.
How It Works
The AI agent uses a LangGraph framework to manage booking sessions, with a checkpointer for state persistence. By implementing a Postgres database, the agent can store bookings and technician data in separate tables, allowing for reliable data access and preventing data loss during process restarts.
The system uses a defined protocol to interact with either an in-memory or Postgres repository, ensuring consistent functionality regardless of the storage method.