Enterprise Knowledge Graph

Semantic representation of an organization's data, files, metadata, business logic, and relationships so AI agents can ground actions in trusted business context.

Key points

• Google Cloud's Knowledge Catalog constructs a unified, dynamic context graph across the business to ground agents in data and semantics ^[src-044].
• Smart Storage and Object Context API tag and enrich files in Google Cloud Storage with metadata before an agent touches them ^[src-044].
• A Knowledge Engine uses Gemini to tag, define logic, and map complex relationships across the enterprise ^[src-044].
• The goal is to provide the missing semantic definition agents need to act on enterprise data rather than only retrieve documents ^[src-044].
• Herk's second-brain maturity model adds a practical threshold: a graph is worth considering when the system needs typed relationships, not just backlinks, such as "works at", "endorsed by", "competitor of", "developed by", or "connects to" ^[src-103].
• The same source warns against graph complexity for its own sake. If a markdown wiki and routing files already answer the real questions, graph infrastructure can be unnecessary overhead ^[src-103].

Related concepts

• Agentic Data Cloud
• Context Quality Engineering
• LLM Knowledge Bases (Karpathy pattern)
• RAG Retrieval Methods
• Machine-Scannable Content
• AI Second Brain Maturity Model
• LLM Wiki vs Semantic RAG

Source references

• ^[src-044] Thomas Kurian — "Welcome to Google Cloud Next '26" (2026-04-22)
• ^[src-103] Nate Herk – "Every Level of a Claude Second Brain Explained" (2026-06-17)