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Google Cloud Professional Agentic Architect Exam Guide
https://www.udemy.com/course/google-cloud-professional-agentic-
architect-exam-guide/
Year : 2026
Language : English
Level : All Levels
Category : IT & Software
Subcategory : IT Certifications
Duration : 5h 30m
Lectures : 45
Rating : 4.3/5 (36 reviews)
Students : 494
INSTRUCTOR(S)
HEADLINE
Build, evaluate, deploy and secure AI agents on Google Cloud.
Full coverage of all 5 exam domains, in the official order
WHAT YOU'LL LEARN
* Build AI agents on Google Cloud two ways ? low-code with
Gemini Enterprise, and in code with the Agent Development
Kit
(ADK)
* Design production RAG pipelines: embeddings, chunking,
similarity scoring, reranking, Vector Search and Agent
Retrieval
* Orchestrate multi-agent systems using MCP and Agent2Agent
(A2A), with sequential, parallel and graph workflows
* Evaluate agents properly ? golden datasets, trajectory vs.
response, ADK evalset, autoraters and continuous evaluation
* Deploy and scale agents on Agent Runtime, Cloud Run and GKE,
and choose correctly between them
* Secure agentic workflows against prompt injection using
Model
Armor, Agent Gateway, Agent Identity and PAB policies
* Use coding agents effectively ? Antigravity, Claude Code on
Google Cloud, MCP servers, custom skills and secure
sandboxes
* Configure agent memory and state with managed sessions and
Agent Platform Memory Bank
* Troubleshoot real agent failures: drift, reasoning loops,
tool
latency, logic errors and hallucinations
* Cover 100% of the Professional Agentic Architect exam guide,
section by section, in Google's own order
REQUIREMENTS
* Comfortable writing code ? Python especially. This is a
professional-level certification, not an entry-level one.
* Working knowledge of Google Cloud fundamentals: IAM, Cloud
Storage, and at least one database service
* Basic familiarity with large language models and how APIs
work
* A Google Cloud account is useful for following along, though
the theory lectures stand on their own
* No prior agent-building experience needed ? every concept is
built up from first principles
WHO IS THIS COURSE FOR
* Developers and cloud architects preparing for the Google
Cloud
Professional Agentic Architect certification
* Engineers who have built a chatbot demo and now need to make
something production-ready
* Cloud professionals with an existing GCP certification
adding
agentic AI to their skill set
* Technical leads who need to evaluate, deploy, secure and
govern agents ? not just build them
* Anyone who wants a structured path through ADK, RAG, MCP,
A2A
and the Google Cloud agent platform
DESCRIPTION
Everything on the Google Cloud Professional Agentic Architect
exam ? and nothing that isn't. This course covers all five
domains of the Professional Agentic Architect certification,
in
the exact order Google's own exam guide presents them. Every
objective is covered. Every in-scope tool is explained.
Structured to match the exam, not to pad the runtime Most
certification courses spend roughly equal time on every topic.
This one doesn't, because the exam doesn't: Section 1 ?
Building
agents using low-code tools · 13% of the exam · 6 lectures
Section 2 ? Using coding agents for application development ·
17% · 7 lectures Section 3 ? Developing custom agents · 33% ·
14
lectures Section 4 ? Evaluating and deploying agentic
workflows
· 22% · 9 lectures Section 5 ? Securing and governing agentic
workflows · 15% · 6 lectures Forty-two lectures, each sized to
what it's actually worth. Section 3 is a third of the exam, so
it gets a third of the course. One worked example, from start
to
finish Instead of five disconnected demos, a single company ?
Sunrise Electronics ? runs through the entire course. You'll
watch them build a low-code support agent, hand tools to a
coding agent, write a custom warranty agent in ADK, connect it
to their own documents with RAG, prove it actually works,
deploy
it to production, and finally survive a security review. By
the
end, you haven't just learned a list of services. You've seen
one real system grow from nothing to production. What you'll
actually cover Building agents (low-code): Gemini Enterprise,
Agent Designer, CX Agent Studio, pages and transition routes,
event handlers and parameters, system instructions, few-shot
and
chain-of-thought prompting, Agent Search, and multimodal
ingestion of images, audio and video. Coding agents:
Antigravity
(CLI, SDK and App), Claude Code on Google Cloud, MCP servers,
custom skills and the Skill Registry, secure sandboxes on
Cloud
Workstations and GKE, plus Agents CLI for operating a whole
fleet. Custom agents in code: Agent design patterns, LLM vs.
SLM
selection, Model Garden and Gemini models, the Agent
Development
Kit (ADK), tools and function calling, callbacks, managed
sessions, Agent Platform Memory Bank, RAG end to end ?
embeddings, chunking, similarity scoring, reranking ? Vector
Search, Agent Retrieval, RAG Engine, Agent Identity, Agent
Registry, the MCP and A2A protocols, and multi-agent
orchestration. Evaluating and deploying: Why agent testing is
nothing like normal software testing, trajectory vs. final
response, golden datasets, response and retrieval quality
metrics, the ADK evalset, the Gen AI evaluation service,
custom
autoraters, continuous evaluation pipelines, choosing between
Agent Runtime, Cloud Run and GKE, Cloud Logging and Cloud
Trace,
and troubleshooting drift, reasoning loops, latency and
hallucinations. Securing and governing: The agent threat
model,
prompt injection, OAuth 2.0 and Auth Manager, principal access
boundary (PAB) policies, Agent Gateway, Model Armor, Agent
Registry governance, human-in-the-loop patterns, and Sensitive
Data Protection. Taught in plain English Every abstract idea
gets a concrete anchor. MCP is explained as USB for agents ?
one
standard plug instead of a different cable for everything.
Sandboxing is giving the agent a workshop, not the run of the
building. A principal access boundary is the wall around the
building, where IAM is just the key you were handed. Nothing
is
used before it's defined, and no lecture runs longer than
eight
minutes. Is this course for you? This is a professional-level
certification. Google's own exam guide describes the ideal
candidate as an experienced developer or architect who can
write
code and has worked with large language models. You'll get the
most from this course if you're comfortable with Python, know
your way around Google Cloud basics like IAM and Cloud
Storage,
and understand roughly how APIs work. You do not need any
prior
experience building agents. Every concept is built up from
first
principles. Ready to start? The agentic architect
certification
is new, and the skills it tests ? ADK, RAG, MCP, A2A, agent
evaluation, agent security ? are the ones every cloud team is
scrambling to build right now. Enroll today and start with
Section 1.
COURSE CONTENT
Chapter 1: Introduction
1. Course Overview
2. Course Resources
Chapter 2: Domain 1: Building Agents using Low-Code Tools
3. Gemini Enterprise & the Low-Code Landscape
4. Pages and Transition Routes
5. Event Handlers, Parameters & Flow Control
6. System Instructions & Prompt Templates
7. Agent Search & Secure Enterprise Data
8. Multimodal Unstructured Data Ingestion
Chapter 3: Domain 2: Using Coding Agents for Application
Development
9. Coding Agents: The Landscape
10. MCP Servers for Coding Agents
11. Custom Skills and the Skill Registry
12. Secure Sandboxes
13. Refactor, Optimize, Patch
14. Customizing Antigravity
15. Antigravity and Agents CLI
Chapter 4: Domain 3: Developing Custom Agents
16. Agent Design Patterns
17. Model Selection
18. Model Garden and Gemini LLMs
19. ADK Fundamentals
20. Tools, Function Calling and Callbacks
21. Sessions, State and Memory Bank
22. Agents CLI Skills
23. RAG Architecture, Embeddings and Chunking
24. Similarity, Reranking and Vector Retrieval
25. RAG Engine and Data Sources
26. Agent Identity and Agent Registry
27. Google Cloud MCP Servers and Integrations
28. MCP and A2A Protocols
29. Multi-Agent Workflows and Handoffs
Chapter 5: Domain 4: Evaluating and Deploying Agentic
Workflows
30. Why Agent Evaluation Is Different
31. Building Test Sets
32. Response and Retrieval Quality Metrics
33. ADK Evalset and the Gen AI Evaluation Service
34. Custom Autoraters and Continuous Evaluation
35. Choosing a Deployment Runtime
36. Deploying and Scaling Production Workloads
37. Observability with Cloud Logging and Cloud Trace
38. Troubleshooting and Optimization
Chapter 6: Domain 5: Securing and Governing Agentic Workflows
39. The Agent Threat Model
40. Authentication and Secure Tool Execution
41. Agent Identity and PAB Policies
42. Agent Gateway
43. Model Armor and Agent Registry Governance
44. Safety Frameworks, HITL and Sensitive Data Protection
Chapter 7: Practice Tests
Chapter 8: Role Plays
Chapter 9: Thank You
45. Congratulations
DATES
Published : 2026-09-04
Last Updated : 2026-09-20
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