Date: 07/14/2025
Okay, this video is exactly the kind of content that gets me fired up about the future of development. It’s all about building smarter AI agents with n8n that can actually understand and query structured data, like spreadsheets, using a hybrid RAG (Retrieval-Augmented Generation) approach. We’re talking about giving our agents the ability to not just semantically search, but to do things like sum columns, filter rows, and perform real SQL queries through natural language!
Why is this valuable? Well, how many times have you built a clunky interface just to let a user run a simple report on some data? This video shows you how to use an AI agent to interpret a user’s natural language request (“What were the total sales in France last month?”) and translate it into an actual SQL query against a Supabase database. The magic is in how the data is ingested and managed – storing structured data in a flexible JSONB column, so you don’t need a rigid schema upfront. Plus, it smartly combines vector search for unstructured data with SQL queries for the structured stuff – the agent decides which to use. It walks through a complete data pipeline, too, covering things like handling data changes in Google Drive and keeping everything synced. No-code is cool and all, but the real power comes when you can seamlessly blend it with robust backend logic.
For me, the most exciting thing is the shift from building rigid UIs and APIs to crafting intelligent agents that can adapt to changing data and user needs. Imagine the possibilities for automating reporting, data analysis, and even complex business workflows! I’m already brainstorming ways to apply this to a reporting project for a client. I’m thinking by setting up a system like this, we can drastically cut down the time spent manually building reports and dashboards. It’s worth experimenting with, as I see it lowering dev time by potentially 50%!