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Google Cloud Professional Agentic Architect
Certification Covered
Google Cloud Certified – Professional Agentic Architect
Stack
Program Includes:
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.
Course Content
1Foundations: LLMs, Agents & the Google Cloud Agent Platform3 Sections
1.1 — LLM & Prompting Essentials
1.1 — LLM & Prompting Essentials
1.2 — Agents, Tools, MCP & A2A in Plain Terms
1.2 — Agents, Tools, MCP & A2A in Plain Terms
1.3 — Google Cloud Agent Platform Map & Lab Setup
1.3 — Google Cloud Agent Platform Map & Lab Setup
2Building Agents Using Low-Code Tools2 Sections
2.1 — Low-Code Agent Workflows in Agent Designer & CX Agent Studio
2.1 — Low-Code Agent Workflows in Agent Designer & CX Agent Studio
2.2 — Connecting Enterprise & Multimodal Data to Gemini Enterprise
2.2 — Connecting Enterprise & Multimodal Data to Gemini Enterprise
3Using Coding Agents for Application Development2 Sections
3.1 — Coding Agents with MCP, Skills & Secure Sandboxes
3.1 — Coding Agents with MCP, Skills & Secure Sandboxes
3.2 — Customizing Antigravity & Extending with Agents CLI
3.2 — Customizing Antigravity & Extending with Agents CLI
4Developing Custom Agents4 Sections
4.1 Part A — ADK Fundamentals & Model Selection
4.1 Part A — ADK Fundamentals & Model Selection
4.1 Part B — Sessions, Memory & Skills
4.1 Part B — Sessions, Memory & Skills
4.2 — RAG Pipelines, Agent Identity & MCP Integrations
4.2 — RAG Pipelines, Agent Identity & MCP Integrations
4.3 — Multi-Agent Orchestration with MCP & A2A
4.3 — Multi-Agent Orchestration with MCP & A2A
5Evaluating and Deploying Agentic Workflows2 Sections
5.1 — Agent Evaluation: Golden Data, Evalsets & Autoraters
5.1 — Agent Evaluation: Golden Data, Evalsets & Autoraters
5.2 — Deploying, Troubleshooting & Monitoring Agents
5.2 — Deploying, Troubleshooting & Monitoring Agents
6Securing and Governing Agentic Workflows2 Sections
6.1 — Agent Identity, OAuth, PAB & Agent Gateway
6.1 — Agent Identity, OAuth, PAB & Agent Gateway
6.2 — Guardrails, Model Armor & Human-in-the-Loop
6.2 — Guardrails, Model Armor & Human-in-the-Loop
7Certification Readiness & Mock Exam1 Sections
7.1 — Mock Exam, Scenario Drills & Exam Strategy
7.1 — Mock Exam, Scenario Drills & Exam Strategy
Overview of Capstone Projects
Enterprise Customer Operations Multi-Agent Platform
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
Agent-Driven Secure Application Modernization
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)
- Building agents using low-code tools (13%) — workflow design, prompt templates and grounded data access.
- Using coding agents (17%) — sandbox security, customizations, refactoring and patch quality.
- Developing custom agents (33%) — ADK design, memory, RAG quality, MCP/A2A and multi-agent coordination.
- Evaluating and deploying (22%) — golden dataset coverage, CI evaluation, runtime choice and observability.
- Securing and governing (15%) — Agent Identity, PAB, Agent Gateway, Model Armor and HITL.
Tech Stack
Career Opportunities
Learning Roadmap
Foundations: LLMs, Agents & the Google Cloud Agent Platform
3 Sections
Building Agents Using Low-Code Tools
2 Sections
Using Coding Agents for Application Development
2 Sections
Developing Custom Agents
4 Sections
Evaluating and Deploying Agentic Workflows
2 Sections
Securing and Governing Agentic Workflows
2 Sections
Certification Readiness & Mock Exam
1 Sections
Certified Google Cloud Professional Agentic Architect
Goal Achieved
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
Program Director
Set the direction of the program and ensure it meets industry and academic excellence standards.

Aashish Pandey
Blue Data Consulting
Program Manager
Manages schedules, coordination, assessments, and ensures smooth course delivery.

Ragini Sankhwar
Blue Data Consulting
Industry Expert/Trainers

Harsh Dalal
Blue Data Consulting
AI Consultant
SME
Covers theory, hands-on labs, assessments, and daily assignments.

Aproova
Blue Data Consulting
Master SME
Specialized guidance for Capstone Projects and advanced implementation.

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)
Program Fee
Complete program access
(After Application Fee + 20% Discount)
*18% GST & Certification cost included
*Important: First pay Application Fee, then you can enroll in Program
Easy EMI Plans
Pay in flexible monthly installments
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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.