Building RAG-Enabled Agents w/ Postgres and Agent Framework
Hosted by Spencer Schneidenbach
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Learn directly from Spencer Schneidenbach
What you'll learn
Discuss the basics of RAG, embeddings, vectors, and search
Learn the basics of AI search primitives and techniques, and how they fit into the big picture.
Implement Agent Memory with PostgreSQL
Learn how PostgreSQL stores agent state, conversation history, and embeddings for context and retrieval.
Perform RAG searches with your AI Agent
Master building your own RAG-enabled AI agents using Microsoft's Agent Framework
Why this topic matters
RAG-enabled AI agents are becoming core building blocks in modern applications. Understanding how to design agents with reasoning, memory, and secure system access enables developers to move beyond simple chatbots and build reliable, scalable AI features that can operate safely in real production environments. Examples will be shown in Python AND C# using Microsoft's Agent Framework!
You'll learn from
Spencer Schneidenbach
President/CTO at Aviron Labs
Spencer Schneidenbach is the President and CTO of Aviron Labs, an AI and software development firm based in the United States. He has been recognized as a Microsoft MVP for his AI expertise and contributions to the community.