Google Cloud COE Multi AI Agent using ADK (Agent Development Kit) built in 26 mintues

In this video I have implemented how in a MNC Google Cloud Center of Excellence (COE) can leverage Multi AI Agent to respond to any RFI, RPI documents where inputs from Cloud Architect, Network Engineer, Security Engineer and DevOps Engineer is included as AI Agent Commands: Install ADK in cloud shell: pip install google-adk Set PATH using export PATH=$PATH:~/.local/bin export GOOGLE_CLOUD_LOCATION=global export GOOGLE_CLOUD_PROJECT=$(gcloud config get-value project) export GOOGLE_GENAI_USE_VERTEXAI=1 mkdir happynewyear cd happynewyear adk web Prompt Task is to build my first multi-AI agent. Will be uploading a PDF document which will be a case study. The moment it is uploaded, we need basically need Four AI agents: 1. Cloud Architect AI Agent 2. Network Engineer AI Agent 3. Security Engineering AI Agent 4. DevOps Engineering AI Agent - which is will implement all Infra recommendation from the previous AI Agents in Terraform Will be leveraging ADK (Agent Development Kit) by Google to implement all those things. It will be a LlmAgent that's a class which I wanted it to be used. Now, all the things need to go in a sequence: Cloud Architect AI Agent -- Network Engineer AI Agent -- Security Engineer AI Agent -- DevOps Engineer AI Agent The main root agent should be using Sequential Agent class from ADK Leverage gemini-2.5-pro as the LLM ADK Link: https://google.github.io/adk-docs/ Topmate.io : https://topmate.io/learn_googlecloud_with_mahesh Chapters 0:00 - Intro 01:38 - ADK & Architecture Overview 03:05 - Install ADK 05:25 - Configure Vertex AI 07:41 - Create New Agent 09:28 - Define Multi-Agent Prompt 14:03 - Upload Case Study 19:41 - Review Workflow Events 20:43 - Architecture & Network Analysis 23:59 - Terraform Code Generation 25:13 - Conclusion