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                                                                                 gemini ai Source diagram: Multi-agent GenAI chat platform for a bank — Coordinator Agent orchestrating Accounts / Transaction / Service agents over MCP servers, fronted by an edge/API layer, with observability, cost tracking, PII redaction, agent evaluation, and a session store. This document maps every box in the diagram to concrete, current (2026) Azure and AWS resources, plus the open-source/framework layer (LangGraph, MCP, evaluation, Neo4j) you'd wire underneath either cloud. Where a component is genuinely cloud-native (e.g., the bank's own IdP, Azure/AWS aren't interchangeable at the same layer), that's called out. 1. Layer-by-Layer Component Map # Diagram comp...

To deploy your agentic AI application across cloud providers, you should treat your infrastructure as code using Terraform . This ensures your databases, vector stores, compute environments, and IAM policies are version-controlled, repeatable, and secure. The Core Terraform Workflow Every Terraform deployment follows the same four-step lifecycle: graph LR A[1. Write .tf Files] --> B[2. terraform init] B --> C[3. terraform plan] C --> D[4. terraform apply] Write: Define your infrastructure components inside .tf files using HashiCorp Configuration Language (HCL). Init: Run terraform init to download the specific cloud providers (AWS, Azure, or GCP) and setup the backend storage. Plan: Run terraform plan to preview exactly what changes, creations, or deletions will happen. Apply: Run terraform apply to execute the plan and provision live cloud resources. Cross-Cloud Terraform Architecture Blueprints Below are production-ready code blocks to deploy the foundati...

Deploying a production-ready, feature-complete Agentic AI application on Google Cloud Platform (GCP) requires leveraging the Vertex AI Agent Builder ecosystem . This managed framework handles long-running multi-agent execution loops, session state persistence, and native Model Context Protocol (MCP) tool discovery. [1, 2, 3] Step 1: Initialize Infrastructure & Core Reasoning Models Set up your compute project boundaries and configure your primary intelligence layers. Enable APIs: Activate ://googleapis.com , ://googleapis.com , ://googleapis.com , and ://googleapis.com inside your Google Cloud Console. Model Selection: Provisions foundation models (such as Gemini 1.5 Pro or Gemini 2.0 Flash ) via Vertex AI Model Garden to execute complex multi-step orchestration workflows. [4, 5, 6] Step 2: Establish the Enterprise Data Connection Layer Connect unstructured, structured, and open data meshes using managed data connectors. [5] Unstructured Data Retrieval (RAG): Create a V...

                                                generated by gemini I would build the GCP version around the same financial-services use case as the AWS architecture, but use the current 2026 Google Cloud stack: Gemini Enterprise Agent Platform, ADK, Agent Runtime, Model Garden, Model Armor, GKE, BigQuery Vector Search, Cloud Storage, VPC Service Controls, IAM, Cloud KMS, Cloud Logging/Monitoring, and MCP/A2A where appropriate. Google's current Agent Platform has evolved from the older Vertex AI-centric terminology and now provides model access, agent development, evaluation, deployment, orchestration and governance. ADK can run agents on Agent Runtime, Cloud Run or GKE. ( Google Cloud ) GCP Enterprise GenAI + Agentic AI Architecture 1. The business problem Let's use the same banking scenario: "Investigate why counterparty ABC breached its exposure limit yesterday and ...