Building Agentic Architectures on Google Cloud Platform

An enterprise agentic architecture on Google Cloud can be organized into experience, reasoning, memory, protocol, and data planes, with security, governance, observability, and evaluation operating across all layers. Google Cloud manages the upper Agent Trust Boundary, where services such as Apigee, Cloud Run, Vertex AI, Agent Engine, Firestore-backed session and memory capabilities, Secret Manager, IAM, Model Armor, and Cloud Trace support agent interaction, orchestration, reasoning, safeguards, and monitoring. The Model Context Protocol serves as the bridge between this environment and external tool providers, allowing agents to invoke standardized tools without directly handling downstream APIs, authentication workflows, or source-specific schemas. Below that bridge, CData Connect AI operates within a separate CData Trust Boundary, virtualizing more than 350 data sources into SQL-oriented, domain-scoped tools controlled through workspaces, connections, toolkits, RBAC, and schema validation. This separation assigns agent permissions to Google Cloud controls and data access permissions to CData controls, while combined audit logs provide traceability from user prompts through tool calls, SQL queries, returned data, and final responses. By using MCP as the integration boundary, the design supports substituting models, agent runtimes, or other components without changing the underlying data connectivity layer.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.