Metric Definitions and Business Glossaries for AI Agents
Vague metric definitions derail AI agents where humans would ask clarifying questions.
Farah Serrano
AI-ready data — Page 5
Data Products
Modern Data Architecture Principles for AI-First Teams
Data quality and architecture decide whether AI projects reach production, not model choice.
Semantic Layer vs Vector Database for AI Querying
Choosing between them depends on what your AI actually needs to answer.
Yuki Novak
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Metadata Management for AI-Consumable Data
AI agents need machine-executable metadata to find reliable data before agents deploy.
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Power BI Semantic Layer Capabilities and Limitations for AI
AI agents can query the semantic layer, but only the data models already defined inside it.
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Semantic Layer Architecture for AI Workloads
Governed definitions cut AI agent errors in half by replacing guesswork with enterprise context.
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Semantic Layer Tools and Platforms Evaluation
Semantic layers could solve AI's real problem—data meaning, not model quality.
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Semantic Layer Examples in Production Data Platforms
How AI agents need governed metric definitions to stop hallucinating on data.
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Structural Metadata vs Semantic Metadata for AI Consumers
Semantic metadata gives AI agents the business context they need to avoid confident mistakes.
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dbt Semantic Layer for AI Agent Querying
Semantic layers ground AI agents in accurate metrics instead of plausible-sounding guesses.
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Why Enterprise AI Initiatives Stall at the Data Layer
Data quality and governance breakdowns, not AI models, doom most enterprise initiatives.
Farah Serrano
Types of Data Integration and Their AI Suitability
Agents need real-time data context that traditional pipelines were never designed to provide.
Nadia Kaur