Building an Agentic Data Layer on Google Cloud: 5 Key Scenarios
Accessing enterprise data is shifting from static reports to dynamic use by autonomous systems. To keep up, organizations must route fragmented data from SaaS, IoT, and legacy sources into secure, scalable endpoints.
However, moving to AI-driven exposure requires more than just connecting an LLM to a database, it requires a fundamental architectural shift to manage security, costs, and semantic accuracy.
What we’ll cover
This article explores the technical evolution of data exposure through five architectural patterns: from manual SQL development to autonomous workflows standardized by the Model Context Protocol (MCP).
While the examples use BigQuery and mocked CRM data, the patterns apply to most enterprise data assets transitioning into an agentic workflow.
The 5 Scenarios of Data Evolution
The transition from static reports to agentic insights is defined by two factors: Trust and complexity.
Trust dictates autonomy: Low-trust environments (like external client-facing apps) require deterministic, hard-coded logic to prevent errors....
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