Businesses want to quickly and safely deploy AI agents to drive revenue, mitigate risk, and optimize capital, all without disrupting mission-critical ERP systems. And to power AI agents, you need more than raw data: You need interoperable data products that act as a single, reusable source of truth, turning cryptic source system records into clear business terms.
Today, we are announcing the general availability of Google Cloud Cortex Framework version 7. This release modernizes your data architecture for agent readiness, helping you quickly deploy, customize, and extend robust data products while simplifying orchestration and reducing the infrastructure overhead of traditional approaches.
While ERP systems hold a wealth of foundational data, turning those transactional records into AI-ready data products is difficult, especially without slowing down core operations. Cortex Framework v7 solves this with purpose-built data product accelerators for SAP. Deployed directly in BigQuery and Knowledge Catalog, these data products feed Gemini Enterprise Agent Platform with accurate business context so your AI agents can execute with high fidelity. It also simplifies data orchestration using a modular, scalable deployment architecture powered by Dataform, an end-to-end experience that helps data teams build, version control, and orchestrate workflows in BigQuery.
Read on for more details about what you’ll find in Cortex Framework v7, how it integrates with the recently released SAP Business Data Cloud Connect for BigQuery, what our customers are saying, and how to get started.
What’s new in Cortex Framework v7
1. Deliver agent-ready data products
Traditional business intelligence (BI) dashboards tell you what happened last quarter, but modern AI models and agents help you act in real time. To power this shift, Cortex Framework v7 packages your enterprise data into semantically rich data products that contain AI-friendly metadata. This enables your agents to reason, orchestrate, and execute high-impact workflows.
By translating raw SAP tables into clear business terms, dynamically ingesting custom fields, and natively handling advanced logic (like SAP TCURX currency decimal shifts), the Framework maintains the high data fidelity required for large language model (LLM) interactions and enterprise analytics.
