Advanced Agentic AI: Production Data Architecture

Overview

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AI isn’t the future anymore production-ready AI systems are. Are you ready to build them? Most developers learn how to call AI models. But the real advantage lies in building systems that retrieve the right data, process context intelligently, and scale reliably in production. That’s the gap this course closes. In this Agentic AI course, you’ll build a complete RAG pipeline using pgVector and PostgreSQL configuring vector databases, storing embeddings, & executing high-performance similarity search. You’ll design intelligent query pipelines with context construction and prompt engineering to generate precise, grounded outputs. You’ll then integrate RAG with MCP to create AI agents capable of handling real customer and order workflows. Finally, you’ll architect a production-ready MongoDB layer designing schemas, optimizing queries, & migrating services from mock data to scalable systems. What makes this different? You’re not learning isolated tools you’re building a real, end-to-end AI architecture used in modern production environments. Perfect for developers and AI engineers who want to move from experimentation to real impact. Enroll now and stay ahead of the curve.

Syllabus

Vector Search & RAG Engine

Learn how to connect NodeJS to pgVector, store embeddings, and implement vector similarity search. Build a RAG engine, generate context, and integrate it as a tool in MCP for real-world AI workflows.

MongoDB Production Architecture

Implement MongoDB with Mongoose, design optimized schemas for customers and orders, and migrate from mock data to production-ready databases. Ensure clean, efficient, and scalable data handling for your agentic AI system.