The Problem Every AI Engineer Faces
You've built AI agent prototypes. They work in notebooks. But how do you deploy them in production?
I faced this exact challenge after building several AI Agent prototypes over the past few months.
The First Critical Step
Convert workflows from notebooks into APIs.
The above video shows you how I tackled this challenge with a real agentic RAG system built using the Db2 LangChain connector.
What You'll See in Action:
GitHub repo setup on macOS
FastAPI deployment process
Live command line testing
Notebook logic converted to production endpoint
Get the Code: Agentic RAG Document Ingestion Code + macOS setup
The Technical Architecture
The agentic RAG workflow has two components:
Document Ingestion Pipeline
Downloads documents from URLs
Extracts and cleans content
Vectorizes the data
Inserts vectors into a Db2 table
Agent RAG Workflow (coming next)
Orchestrates the agent workflow using LangGraph
What's Coming Next
This is the first in my "AI Agents in Production" series. Each video will build toward a complete production deployment of this example AI Agent.
