Google Cloud Professional Agentic Architect
Courses/Deep-Tech AI

Google Cloud Professional Agentic Architect

Certification Covered

Google Cloud Certified – Professional Agentic Architect

Stack

Gemini EnterpriseADKAntigravity+6

Program Includes:

Instructor-led, lab-heavy
Hands-on
24x7 Active
40 Hours

Description

Google Cloud Professional Agentic Architect is a 40-hour, instructor-led program for developers, cloud architects and AI engineers who design and run agentic workflows on Google Cloud. A foundations module comes first, five modules then follow the official exam guide section by section, and a final module prepares you with a full mock exam. Through 40+ hands-on labs, you will build low-code agents in Gemini Enterprise, customize coding agents such as Antigravity, develop custom agents with ADK, connect them through MCP and A2A, evaluate and deploy them on Agent Runtime, and secure them with Agent Identity, Agent Gateway and Model Armor. Every section starts with a foundational topic, and the program ends with two enterprise capstone projects.

Course Outcomes

Upon completion, participants will be able to:

  • Configure low-code agents in Gemini Enterprise Agent Designer and CX Agent Studio, grounded in enterprise data through Agent Search
  • Customize coding agents such as Antigravity and Claude Code with MCP servers, skills, hooks and subagents inside secure sandboxes
  • Build custom agents with ADK, choosing the right model, sessions, Memory Bank and skills for each use case
  • Design RAG pipelines and multi-agent workflows with RAG Engine, Vector Search, Agent Retrieval, MCP and A2A
  • Evaluate agents with golden datasets and ADK evalsets, and deploy them on the right runtime: Agent Runtime, Cloud Run or GKE
  • Secure and govern agents with Agent Identity, Agent Gateway and Model Armor, plus OAuth 2.0, PAB policies and human-in-the-loop controls

Prerequisites

  • Basic Python programming: Data types, functions, loops and conditionals, classes, error handling, virtual environments and reading/writing JSON.
  • APIs and JSON: REST APIs, HTTP methods and status codes, JSON and JSON Schema.
  • Google Cloud basics: Projects, IAM roles and service accounts, Cloud Storage, BigQuery and the gcloud CLI. Associate Cloud Engineer–level knowledge is helpful but not mandatory.
  • Developer tooling: VS Code or a similar IDE, terminal usage, Git and GitHub (clone, commit, push), and Docker basics.
  • No prior agent experience required: LLM, agent, MCP and Google Cloud platform basics are covered in Module 0, and every section opens with a foundational topic.
  • Lab environment: A Google Cloud project with billing enabled, access to Gemini models on Agent Platform and a Gemini Enterprise license for Section 1 labs.

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Course Content

1
Foundations: LLMs, Agents & the Google Cloud Agent Platform

1.1 — LLM & Prompting Essentials

1.2 — Agents, Tools, MCP & A2A in Plain Terms

1.3 — Google Cloud Agent Platform Map & Lab Setup

2
Building Agents Using Low-Code Tools

2.1 — Low-Code Agent Workflows in Agent Designer & CX Agent Studio

2.2 — Connecting Enterprise & Multimodal Data to Gemini Enterprise

3
Using Coding Agents for Application Development

3.1 — Coding Agents with MCP, Skills & Secure Sandboxes

3.2 — Customizing Antigravity & Extending with Agents CLI

4
Developing Custom Agents

4.1 Part A — ADK Fundamentals & Model Selection

4.1 Part B — Sessions, Memory & Skills

4.2 — RAG Pipelines, Agent Identity & MCP Integrations

4.3 — Multi-Agent Orchestration with MCP & A2A

5
Evaluating and Deploying Agentic Workflows

5.1 — Agent Evaluation: Golden Data, Evalsets & Autoraters

5.2 — Deploying, Troubleshooting & Monitoring Agents

6
Securing and Governing Agentic Workflows

6.1 — Agent Identity, OAuth, PAB & Agent Gateway

6.2 — Guardrails, Model Armor & Human-in-the-Loop

7
Certification Readiness & Mock Exam

7.1 — Mock Exam, Scenario Drills & Exam Strategy

Overview of Capstone Projects

Enterprise Customer Operations Multi-Agent Platform

Objective: Design, build and govern a production multi-agent platform that combines a low-code customer front door with ADK agents, grounded answers and end-to-end security controls.

What you will build

  • Build a low-code front door in CX Agent Studio, with a flow-based agent for order status and returns that uses pages, transition routes and event handlers, guided by few-shot and chain-of-thought templates.
  • Ground answers in enterprise knowledge by ingesting policy PDFs, product images and support-call audio into Agent Search and a RAG Engine corpus, with reranking for product lookup.
  • Develop an ADK coordinator agent that delegates to billing, returns and inventory sub-agents using sequential and parallel workflows, with the inventory agent exposed over A2A.
  • Connect tools through MCP: a Google Cloud MCP server for BigQuery and a custom MCP server for a third-party CRM deployed on Cloud Run and registered in Agent Registry.
  • Add sessions and long-term memory with managed sessions and Memory Bank on Agent Runtime.
  • Secure and govern the system with Agent Identity, OAuth 2.0 through Auth Manager, a PAB policy, Agent Gateway egress policies, Model Armor templates and HITL approval for refunds above a threshold.
  • Evaluate and deploy with an ADK evalset of 50+ golden and edge cases, a CI evaluation gate, deployment to Agent Runtime, and Cloud Logging and Cloud Trace dashboards.

Deliverables

  • Architecture and ADR — a system diagram and decision record covering model, runtime and security choices
  • Working agent system — CX Agent Studio front door plus ADK coordinator and sub-agents deployed on Agent Runtime
  • Evaluation report — evalset results, the CI evaluation gate and monitoring dashboards
  • Security and governance pack — Agent Identity, PAB, Agent Gateway policies, the Model Armor template and HITL evidence

Tech Stack

CX Agent StudioADKAgent SearchRAG EngineA2A / MCPAgent RuntimeAgent GatewayModel Armor

Agent-Driven Secure Application Modernization

Objective: Use customized coding agents in secure sandboxes to modernize a legacy application, then build, deploy and continuously evaluate an internal ADK agent with Agents CLI.

What you will build

  • Set up secure coding-agent sandboxes on Cloud Workstations with restricted egress, plus Antigravity's terminal sandbox and approval rules for risky commands.
  • Customize Antigravity with team rules, reusable skills, a lifecycle hook that runs lint and security scans, a security-reviewer subagent, and a plugin that bundles them for the team.
  • Modernize a legacy application with Antigravity and Claude Code on Google Cloud: add tests, refactor a module into a service, reduce container size and cold starts on Cloud Run, and patch an injection flaw and a vulnerable dependency.
  • Build a "DevOps assistant" ADK agent with Agents CLI that answers deployment questions from Cloud Logging and BigQuery data, then deploy and evaluate it through Agents CLI.
  • Pick and justify the runtime by comparing Cloud Run, GKE and Agent Runtime on cost, latency and operational effort.
  • Create a continuous evaluation pipeline that scores the assistant's tool execution against success criteria and blocks releases on regression.

Deliverables

  • Source repository — agent code, Antigravity rules, skills, hooks, subagent and plugin definitions, and MCP configuration
  • Modernization report — before/after test coverage, vulnerabilities fixed, image size and latency
  • CI/CD pipeline — workflow configuration with automated evaluation gates and stored results
  • Architecture Decision Record — 600–900 words on model selection, runtime choice, security controls and trade-offs

Evaluation Rubric (aligned to exam weightage)

  1. Building agents using low-code tools (13%) — workflow design, prompt templates and grounded data access.
  2. Using coding agents (17%) — sandbox security, customizations, refactoring and patch quality.
  3. Developing custom agents (33%) — ADK design, memory, RAG quality, MCP/A2A and multi-agent coordination.
  4. Evaluating and deploying (22%) — golden dataset coverage, CI evaluation, runtime choice and observability.
  5. Securing and governing (15%) — Agent Identity, PAB, Agent Gateway, Model Armor and HITL.

Tech Stack

AntigravityClaude CodeAgents CLICloud WorkstationsCloud RunGKEGen AI evaluation

Career Opportunities

Learning Roadmap

1

Foundations: LLMs, Agents & the Google Cloud Agent Platform

3 Sections

2

Building Agents Using Low-Code Tools

2 Sections

3

Using Coding Agents for Application Development

2 Sections

4

Developing Custom Agents

4 Sections

5

Evaluating and Deploying Agentic Workflows

2 Sections

6

Securing and Governing Agentic Workflows

2 Sections

7

Certification Readiness & Mock Exam

1 Sections

Certified Google Cloud Professional Agentic Architect

Goal Achieved

Enrolling for Upcoming March Batches

Weekdays (Mon-Fri)

Starting from 16th November

Duration: 2 Hours per day

Weekend (Sat & Sun)

Starting from 21st November

Duration: 4 Hours per day

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Meet Our Leaders

Leadership

Program Director

Set the direction of the program and ensure it meets industry and academic excellence standards.

Aashish Pandey

Aashish Pandey

Blue Data Consulting

Leadership

Program Manager

Manages schedules, coordination, assessments, and ensures smooth course delivery.

Ragini Sankhwar

Ragini Sankhwar

Blue Data Consulting

Industry Expert/Trainers

Harsh Dalal

Harsh Dalal

Blue Data Consulting

AI Consultant

SME

Covers theory, hands-on labs, assessments, and daily assignments.

Aproova

Aproova

Blue Data Consulting

Master SME

Specialized guidance for Capstone Projects and advanced implementation.

Ahmed

Ahmed

Blue Data Consulting

Mentor

Dedicated support to help all learners pass the certification exam.

Transparent Pricing

Invest in your future with our flexible payment options

Application Fee

5% of Total Fee (Non-refundable)

₹5,160

Program Fee

Complete program access

₹129,000SAVE 20%

(After Application Fee + 20% Discount)

₹98,040

*18% GST & Certification cost included

Industry-recognized certification
Lifetime access to materials
24*7 Technical Support

*Important: First pay Application Fee, then you can enroll in Program

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Starting from

₹4,754/month

Frequently Asked Questions

Who is the target audience for this course?

The course is designed for developers, cloud architects, and AI engineers who want to design and run agentic workflows on Google Cloud.

What certification does this program cover?

It prepares you for the Google Cloud Certified – Professional Agentic Architect certification exam.

Do I need prior experience with AI agents to enroll?

No prior agent experience is required. LLM, agent, MCP, and platform basics are covered in the foundational Module 1.

What are the technical prerequisites?

You should have basic Python programming skills, familiarity with REST APIs/JSON, Google Cloud basics (Projects, IAM, Cloud Storage, BigQuery), and experience with developer tooling like VS Code, Git, and Docker.

What tools and technologies are taught in the course?

The tech stack includes Gemini Enterprise, ADK, Antigravity, Agents CLI, Agent Runtime, MCP & A2A protocols, Agent Gateway, Model Armor, Agent Search, and RAG Engine.

Is the course hands-on?

Yes, it includes 40+ guided hands-on labs and two enterprise capstone projects over a 40-hour instructor-led delivery format. 

Where will I deploy the custom agents I build?

You will learn to deploy agents on Agent Runtime, Cloud Run (for containerized agents and MCP/A2A servers), and GKE (Google Kubernetes Engine).

How does the course handle agent security and guardrails?

Module 6 is dedicated to securing and governing agents using Agent Identity, OAuth 2.0, Agent Gateway, Model Armor templates (against prompt injection/jailbreaks), and Human-in-the-Loop (HITL) confirmation.

Will I build any real-world projects?

Yes, you will complete two capstones: building an "Enterprise Customer Operations Multi-Agent Platform" and executing an "Agent-Driven Secure Application Modernization". 

How does the course teach agent evaluation?

You will learn to evaluate agents using golden datasets, ADK evalsets (scoring tool-trajectory and response-match), and the Agent Platform Gen AI evaluation service, as well as setting up continuous CI evaluation pipelines. 

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