How to Build a production-ready RAG AI agent

GCP credit → https://goo.gle/handson-ep2-lab2 Codelab & source code → https://goo.gle/scholar Try Google ADK → https://goo.gle/4bPEHej In this episode, Ayo and Annie go from structured data to a fully deployed, data-aware RAG agent, and we cover a LOT of ground. Starting where they left off from last episode (BigQuery + BQML.GENERATE_TEXT), the duo now wire up the full backend for an AI agent: a vector database, an embedding pipeline, a RAG retrieval system, and a production ready Cloud Run deployment. 🛠️ *What we build:* * Cloud SQL for PostgreSQL with pgvector for semantic search * A containerized Apache Beam pipeline on Dataflow to batch-process text and generate Gemini embeddings * A RAG retrieval layer that lets the agent query vectorized knowledge * An ADK based agent that answers questions using that knowledge * A Cloud Run deployment with proper security and scalability settings This is hands-on, infrastructure-meets AI content. you'll leave with a real, working pattern you can adapt for your own projects. Chapters: 0:00 - Intro 1:41 - (RAG) Retrieval Augmented Generation and chunking 4:40 - Data project overview 4:52 - Similarity search 6:40 - RAG in BigQuery 11:56 - [BQML] ML Generate in Big Query 19:46 - OLAP & OLTP 24:21 - AI in CloudSQL 28:38 - Index using HNSW 31:29 - Scaling with data pipeline 36:46 - Apache Beam 53:02 - RAG agent With CloudSQL 1:09:52 - Flight the BOSS with A2A More resources: AI in CloudSQL → https://goo.gle/4uRlm5v Apache Beam → https://goo.gle/3O6OJzY ADK Sample → https://goo.gle/4rQKWVn Watch more Hand on AI → https://goo.gle/HowToWithGemini 🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #Gemini #GoogleCloud Speakers: Ayo Adedeji, Annie Wang Products Mentioned: Agent Development Kit, Dataflow