How do you craft a great AI knowledge base?
How do you build a knowledge base for AI agents?
Excited to share my latest for InfoWorld, which explores what goes into creating a solid AI agent knowledge base — from the types of data it should contain to the retrieval mechanisms and architecture patterns that support reliable agentic behavior.
Shared context becomes especially important once you introduce multiple agents into a workflow. So this piece digs into structured, semi-structured, and unstructured data sources, how vector search, graph traversal, and RAG-style retrieval fit in, and why designing the right knowledge graph or abstraction layer matters.
The biggest hurdle? Keeping the knowledge fresh. "Freshness, or lack thereof, is the silent killer of AI knowledge systems," says AJ Sunder.
This is a fascinating and fast-emerging area of agentic AI, and it’s interesting to see early patterns — especially around design and architecture — begin to take shape.
Check it out and, as always, feel free to let me know what you think. Also, if you would like to be featured in one of my articles, I'm always looking for new voices to connect with! Consider signing up to my newsletter, Under The Byline for feature opportunities.
As we fall deeper into the agentic AI rabbit hole, what should I explore next?
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