I Built a Price Infrastructure API for AI Agents — Here's What I Learned

1. 1Design a data schema to store pricing data using a database like PostgreSQL
2. 2Build a RESTful API using Node.js and Express.js to handle requests and responses
3. 3Implement data processing and filtering to handle missing or outdated pricing data
4. 4Integrate the API with AI agents using APIs like GraphQL or gRPC
5. 5Test and deploy the API using containerization with Docker and Kubernetes

Developers and data scientists working with AI agents can benefit from this API to improve pricing accuracy and reliability. The team can use this API to fetch and process pricing data efficiently

💡 Building a price infrastructure API for AI agents requires careful consideration of data schema design, API development, and integration to ensure reliable and accurate pricing data

🤖 Built a Price Infrastructure API for AI agents! 📊 Learned about data schema design, API development, and integration with AI agents 💻