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Google Cloud Professional Agentic Architect

Principles, Products and Practice for Passing the PAA Exam

Google Cloud Professional Agentic Architect
This book is 60% completeLast updated on 2026-09-20
Daniel Vaughan

Learn to design, build and govern agentic systems as you prepare for the Google Cloud Professional Agentic Architect exam. Study practical architectures and worked examples, then test your understanding with 100 original questions aligned with the public exam objectives.

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About

About

About the Book

An agent demonstration is one thing. Designing a system that acts within its authority, uses trustworthy evidence and recovers safely from failure is an architectural challenge.

Google Cloud Professional Agentic Architect connects the public exam requirements to the architectural decisions behind designing, building and operating agentic applications.

Across fifteen focused chapters, you will learn how to:

  • Choose between low-code experiences, coding-agent environments and custom agent services
  • Design workflows, tool contracts and multiagent coordination with Agent Development Kit (ADK), Model Context Protocol (MCP) and Agent2Agent (A2A)
  • Ground responses in enterprise knowledge while preserving permissions and source authority
  • Separate conversation context, durable memory and authoritative business state
  • Evaluate agent behaviour, diagnose failures and make evidence-based release decisions
  • Apply identity, security and governance controls across tools, services and deployment environments

Worked scenarios apply these decisions to an enterprise knowledge assistant, a governed coding environment and a custom multiagent service. Configuration examples, architecture diagrams and product comparisons explain what to choose, why it fits and where its limits lie.

The book includes 100 original review and capstone questions with answer explanations, seven practical checkpoints and an objective coverage matrix to guide revision. References point to documentation available without signing in.

Written for developers, architects and platform engineers, this book is a focused exam-preparation guide and a practical reference for agentic architecture on Google Cloud.

This independent study guide is not an official Google publication. Its original practice questions do not reproduce certification assessment items. The book does not guarantee examination success.

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About the Author

Daniel Vaughan

Daniel Vaughan is a technology leader and software architect based in the United Kingdom, specialising in agentic AI. He has spent approaching thirty years across enterprise, startup, and academic settings, with a career-long focus on engineering quality and developer productivity. His work now centres on the shift where AI stops being an experiment and becomes core to how organisations build software.

Daniel is Head of Forward Deployed Engineering at HCLTech AI Labs, where he leads a global practice embedding engineers and coding agents inside complex enterprise environments to take production AI from prototype to live systems. He built the practice from the ground up, running lean regional pods in which a shared architect, a small number of engineers, and a team of agents working through tools such as Codex, Claude Code, Cursor, and GitHub Copilot deliver at the output of a much larger team.

Before HCLTech AI Labs, Daniel was director of software engineering at Mastercard in London, leading cloud strategy and architecture for real-time payment products in a highly regulated financial services environment. Earlier, he spent eight years at the European Bioinformatics Institute in Cambridge, moving from software engineer into engineering leadership, working on the same problems of software quality and developer productivity that he now solves with agentic tooling.

He is the author of Cloud Native Development with Google Cloud (O'Reilly, 2024) and Ext GWT 2.0: Beginner's Guide (Packt, 2010), a Google Developer Expert, and a Green Software Champion. He writes about agentic engineering and AI-assisted development at blog.danielvaughan.com.

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Contents

Table of Contents

Preface

  1. Who This Book Is For
  2. The Reading Route
  3. Recurring Scenario Families
  4. Scope and Evidence
  5. Conventions
  6. Acknowledgments

Chapter 1. The Exam, the Architect and the Scenarios

  1. Learning Objectives
  2. The Architect’s Responsibilities
  3. Reading the Public Exam Blueprint
  4. Three Classes of Agent Work
  5. The Three Recurring Scenario Families
  6. Preparation Evidence
  7. A Repeatable Scenario-Reading Method
  8. Personal PAA Study Map
  9. Exam Judgement
  10. Summary
  11. Review Questions

Chapter 2. Agentic Systems and Workflow Architecture

  1. Learning Objectives
  2. The Agent Loop and Its Environment
  3. Deterministic versus Model-Directed Control
  4. State-Based Workflows
  5. Sequential, Parallel and Graph Patterns
  6. Delegation and Human Intervention
  7. Termination and Recovery
  8. Custom Service-Resolution Workflow
  9. Exam Judgement
  10. Summary
  11. Review Questions

Chapter 3. Models, Prompts, Context and Memory

  1. Learning Objectives
  2. Model Selection and Hosting
  3. Prompt Engineering and System Instructions
  4. Output Contracts and Structured Generation
  5. Context Engineering
  6. Context, Sessions, Checkpoints and Memory
  7. Model Routing and Fallback
  8. Worked Maintenance-Assistant Design
  9. Exam Judgement
  10. Summary
  11. Review Questions

Chapter 4. Tools, Protocols and Multiagent Coordination

  1. Learning Objectives
  2. Capability Contracts
  3. Execution Boundaries
  4. Safe Side Effects
  5. MCP Capability Access
  6. Governed Discovery with Agent Registry
  7. A2A Peer Collaboration
  8. Coordination Contracts
  9. Untrusted Results and Consequential Actions
  10. Worked Service-Resolution Design
  11. Exam Judgement
  12. Summary
  13. Review Questions

Chapter 5. Enterprise Knowledge and Retrieval

  1. Learning Objectives
  2. Knowledge Authority Before Search
  3. Ingestion and Representation
  4. Candidate Retrieval
  5. Filtering, Reranking and Context Assembly
  6. Grounded Answers and Knowledge Lifecycle
  7. Choosing a Knowledge Strategy
  8. Retrieval Quality and Failure Diagnosis
  9. Worked Policy-Assistant Investigation
  10. Exam Judgement
  11. Summary
  12. Review Questions

Chapter 6. Agent Evaluation, Reliability and Observability

  1. Learning Objectives
  2. Success Before Implementation
  3. Representative Evaluation Sets
  4. Responses, Retrieval and Trajectories
  5. Evaluation Tools and Execution Boundaries
  6. Evaluator Selection and Calibration
  7. Continuous Evaluation and Reliability
  8. Logs, Metrics and Traces
  9. Diagnostic Method
  10. Worked Freight Evaluation Contract
  11. Exam Judgement
  12. Summary
  13. Review Questions

Chapter 7. Identity, Security, Safety and Governance

  1. Learning Objectives
  2. Actors and Authority
  3. Authentication, Authorisation and Delegation
  4. Agent Threat Models
  5. Execution Isolation
  6. Agent Gateway and Governed Traffic
  7. Content and Data Protection
  8. Governance and Accountable Intervention
  9. Policy Placement and Composition
  10. Security Verification
  11. Worked Customer Address Change
  12. Exam Judgement
  13. Summary
  14. Review Questions

Chapter 8. Low-Code Experiences and Models

  1. Learning Objectives
  2. Experience Before Implementation
  3. A Bounded Enterprise Assistant
  4. Low-Code Control Boundaries
  5. Conversation State and Recovery
  6. Authorised Enterprise Retrieval
  7. Retrieval Completion and Generation
  8. Multimodal Evidence and Freshness
  9. Acceptance and Change Pressure
  10. Gemini Model Selection
  11. Model Garden
  12. Exam Judgement
  13. Summary
  14. Review Questions

Chapter 9. Agent Development and Governed Coding Workflows

  1. Learning Objectives
  2. Four Different Product Responsibilities
  3. Agent Development Kit
  4. Agents CLI in Agent Platform
  5. Antigravity
  6. A Bounded Coding Capability
  7. Developer Journeys and Risk Boundaries
  8. Tools, Credentials and Sandboxes
  9. Evidence Contracts for Engineering Tasks
  10. Worked Exercise: A Bounded Remediation Specification
  11. Shared Capabilities and Agents CLI
  12. Skill Registry
  13. Rollout and Operational Review
  14. Exam Judgement
  15. Summary
  16. Review Questions

Chapter 10. Retrieval and Grounding Services

  1. Learning Objectives
  2. The Retrieval Responsibility Chain
  3. Retrieval Quality Before Product Selection
  4. RAG Engine
  5. Agent Retrieval
  6. Vector Search 1.0
  7. Agent Search at the Experience Boundary
  8. Retrieval Operations and Failure Diagnosis
  9. Worked Product Decision
  10. Exam Judgement
  11. Summary
  12. Review Questions

Chapter 11. Data, State and Memory Services

  1. Learning Objectives
  2. Data Contracts Before Products
  3. Selection by Operation
  4. Product Boundaries and Selection
  5. Sessions, Durable State and Long-Term Memory
  6. Integrating Stores Safely
  7. Worked Freight-Refund Recovery
  8. Failure Diagnosis Across Stores
  9. Exam Judgement
  10. Summary
  11. Review Questions

Chapter 12. Runtime and Deployment

  1. Learning Objectives
  2. Start with the Workload
  3. Framework and Runtime Are Different Decisions
  4. Agent Runtime
  5. Cloud Run
  6. Google Kubernetes Engine
  7. Revisions, Rollouts and Recovery
  8. Diagnosing Runtime Symptoms
  9. Shared Dependencies and Total Cost
  10. Worked Runtime Selection
  11. Migration Triggers
  12. Exam Judgement
  13. Summary
  14. Review Questions

Chapter 13. Requirements, Architecture and Scenario Judgement

  1. Learning Objectives
  2. Extracting the Decision with AADCE
  3. Outcomes and Decision Records
  4. Constraints with Owners and Evidence
  5. Control Placement and Product Fit
  6. A Worked Field-Service Brief
  7. Testing Each Answer Against Requirements
  8. Multiple-Response and Timed Practice
  9. Diagnosing Study Weaknesses
  10. Exam Judgement
  11. Summary
  12. Review Questions

Chapter 14. Production Readiness and Governed Operation

  1. Learning Objectives
  2. Release Contracts and Critical Vetoes
  3. Evidence for One Candidate
  4. Failure Tests Before Promotion
  5. Authority and Routing Evidence
  6. Controlled Release and Recovery
  7. Diagnosis from Competing Explanations
  8. Bounded Optimisation and Retesting
  9. Exam Judgement
  10. Summary
  11. Review Questions

Chapter 15. Capstone Practice Scenarios

  1. Learning Objectives
  2. A Diagnostic Practice Method
  3. The Global HR Knowledge Assistant
  4. Governed Coding-Agent Rollout
  5. A Custom Multiagent Service
  6. Retrieval-Quality and Governance Incident
  7. Deployment, Scale and Cost Incident
  8. Delegated Authority and Security Review
  9. Exam Judgement
  10. A Self-Diagnostic Scorecard
  11. Summary

Appendix A: Answers and Commentary

  1. Chapter 1: The Exam, the Architect and the Scenarios
  2. Chapter 2: Agentic Systems and Workflow Architecture
  3. Chapter 3: Models, Prompts, Context and Memory
  4. Chapter 4: Tools, Protocols and Multiagent Coordination
  5. Chapter 5: Enterprise Knowledge and Retrieval
  6. Chapter 6: Agent Evaluation, Reliability and Observability
  7. Chapter 7: Identity, Security, Safety and Governance
  8. Chapter 8: Low-Code Experiences and Models
  9. Chapter 9: Agent Development and Governed Coding Workflows
  10. Chapter 10: Retrieval and Grounding Services
  11. Chapter 11: Data, State and Memory Services
  12. Chapter 12: Runtime and Deployment
  13. Chapter 13: Requirements, Architecture and Scenario Judgement
  14. Chapter 14: Production Readiness and Governed Operation
  15. Chapter 15: Capstone Practice Scenarios

Appendix C: Glossary of Agentic Architecture Terms

  1. Agents and Capabilities
  2. Workflows and State
  3. Models and Knowledge
  4. Evaluation and Operation
  5. Identity and Governance

Appendix D: Product-Selection Index

Appendix E: Exam-Objective Coverage Matrix

  1. Building Agents Using Low-Code Tools
  2. Using Coding Agents for Application Development
  3. Developing Custom Agents
  4. Evaluating and Deploying Agentic Workflows
  5. Securing and Governing Agentic Workflows

Appendix F: Product-Name Cross-Reference

Appendix G: Checking Sources and Technical Assumptions

  1. Evidence and Its Limits
  2. Rechecking a Decision

Appendix H: Practical Study Evidence

  1. Bounded Exercises
  2. Evidence Record and Cleanup

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