Build a strong foundation for Agentforce for Analytics success with well-structured, well-designed semantic models.
- Design Semantic Models for AI Readiness
A well-designed semantic model is the key to getting accurate and relevant insights with agentic analytics in Tableau Next. It helps your analytics agents understand the data and confidently answer questions. When designing your semantic model, build on a foundation of AI readiness standards and well-established industry best practices for data modeling and data quality. - Build an AI-Ready Semantic Model
A well-designed semantic model helps analytics agents understand data and confidently answer questions. You'll need to make your semantic model AI-ready to provide real-world context and reduce ambiguity for your agents. Success with using Tableau Agent in Tableau Next requires the agent to be grounded with an AI-ready semantic model. - Optimize and Test a Semantic Model
Building, enriching, and testing a semantic model is an iterative process rather than a linear one. As you define and refine the semantic model, you can use various feedback mechanisms available in the Semantic Model Builder to make incremental adjustments. As you fine-tune your approach, each improvement can lead to better operational outcomes.