Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 10·5 min read
Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong
TL;DR
Building an agent-ready data warehouse reveals that traditional architectures often fail to provide essential business rules, leading to incorrect action recommendations despite valid data queries.
✦ Why It Matters
Evaluate your data warehouse architecture to ensure it includes necessary business rules for accurate decision-making.
Key Takeaways
- Define a decision contract for AI agents to clarify data usage.
- Implement a semantic interface to guide agents in data interpretation.
- Establish a safety boundary for query execution to prevent errors.
- Start with specific decisions to test AI recommendations before scaling.
How It Works
A decision contract outlines the parameters for how AI agents can use data, including approved sources and completeness checks. This structured approach ensures that agents have the necessary context to make accurate recommendations.