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How to Build Production-Ready AI Agents: MongoDB Atlas + Google Vertex AI
In this episode, Michael Lynn (MongoDB) and Yang Li (Google Cloud) break down the architectural blueprint for building intelligent, production-grade applications. Move beyond simple RAG (Retrieval-Augmented Generation) and explore the world of AI Agents.
What you’ll learn:
The Google Cloud AI stack: Vertex AI, Agent Space, and Model Garden.
Deep-dive integration: Connecting MongoDB Atlas with BigQuery and Dataflow.
Real-world Demo: Building a grocery store AI assistant using Gemini and Vector Search.
Startup Perks: How to access up to $350k in Google Cloud credits and $10k in MongoDB credits.
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By MongoDB
4.9
7373 ratings
In this episode, Michael Lynn (MongoDB) and Yang Li (Google Cloud) break down the architectural blueprint for building intelligent, production-grade applications. Move beyond simple RAG (Retrieval-Augmented Generation) and explore the world of AI Agents.
What you’ll learn:
The Google Cloud AI stack: Vertex AI, Agent Space, and Model Garden.
Deep-dive integration: Connecting MongoDB Atlas with BigQuery and Dataflow.
Real-world Demo: Building a grocery store AI assistant using Gemini and Vector Search.
Startup Perks: How to access up to $350k in Google Cloud credits and $10k in MongoDB credits.