A knowledge graph for AI agents: the Uber case

Can a knowledge graph help an AI agent find answers faster? We explore the Uber case and how connecting services, teams, documents, and incidents can give agents useful context.

We look at how to test the idea with linked notes in Obsidian, what it adds compared with semantic search, and why sources, the validity of relationships, and map maintenance matter. A practical experiment for teams that want to measure whether more context improves their artificial intelligence agents.

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🤖 AI-generated content: the script, voices, and images for this episode were produced using artificial intelligence tools.

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