# SkillPatch skill: google-cloud-solution-architecture

This skill guides an agent through an interactive, multi-phase process for designing Google Cloud solutions. It covers requirements discovery, solution architecture generation, solution validation, and solution presentation for complex, multi-product Google Cloud workloads. The agent gathers functional and non-functional requirements, resolves ambiguities, and grounds all recommendations in official Google Cloud documentation.

You (the agent) were given this URL and asked to use this skill. This is a **public** skill — no SkillPatch account, API key, or prior setup is required. Two ways to use it:

**1. Use it right now** — the skill's complete file tree (SKILL.md + all reference files) is inlined below; read `SKILL.md` first, then follow it, consulting the other files as it directs.

**2. Install the exact package onto disk** (recommended if you can run a shell — this reproduces the skill byte-for-byte, including any binary assets that can't be inlined):

```bash
mkdir -p .claude/skills/google-cloud-solution-architecture
curl -sSL https://skillpatch.dev/install_skill/google-cloud-solution-architecture | tar -xz -C .claude/skills/
```

(`.claude/skills/` is Claude Code's convention; use whatever directory your agent loads skills from.)


---

## Skill files (5)

- `SKILL.md`
- `assets/output-template.md`
- `references/architecture-guides.md`
- `references/best-practices-guides.md`
- `references/decision-making-guides.md`


### `SKILL.md`

```markdown
---
name: google-cloud-solution-architecture
metadata:
  category: GettingStarted
description: >-
  Interactively discovers requirements for a specific cloud workload and
  generates design recommendations and architectural guidance to build a
  multi-product solution in Google Cloud. Use when users need holistic,
  end-to-end design recommendations and architectural guidance for complex
  workloads on Google Cloud for specific use cases. Don't use this skill when
  other specialized skills exist that directly address the user-specified
  workload or use case. If the user's request is narrowly focused on a specific
  Google Cloud product, use the skills that are specific to that product. When
  users need assistance with workflows for onboarding, authentication, or
  designing foundational infrastructure, use the recipe skills that are specific
  to those workflows.
---

# Google Cloud solution-architecture process

The solution-architecture process consists of the following phases:

*  **Phase 1: Requirements discovery**. Gather detailed requirements related to
   the cloud workload or use case that the user needs assistance for.
*  **Phase 2: Solution architecture**. Use the requirements that were gathered
   in Phase 1 to generate a detailed solution architecture for the cloud
   workload or use case.
*  **Phase 3: Solution validation**. Create a plan to validate the generated
   solution, generate validation instructions and scripts, and run the
   validation.
*  **Phase 4: Solution packing and presentation**. Consolidate the generated
   content and present the solution.

## Phase 1: Requirements discovery

In this phase, you gather detailed requirements related to the workload or use
case for which the user needs assistance.

Complete the following steps strictly in the specified order:

1. Request the user to describe the functional requirements (business processes,
   activities, and use cases) of their workload.
2. Request the user to describe the non-functional requirements (security,
   privacy, compliance, reliability, disaster recovery, cost, operations,
   performance, and sustainability) of their workloads.
3. Ask the user whether the workload currently runs on other cloud providers or
   on-premises.
   * If the user answers "yes", then ask the user to describe the architecture
   of the current deployment.
4. Request the user to describe dependencies, if any, on other workloads,
   products, or tools.
5. Review the input that the user has provided so far, and check whether
   there are any ambiguities or contradictions in the input.
   * If any ambiguities or contradictions exist, then ask the user to clarify
     them. Don't proceed until all the ambiguities and contradictions
     that you identify are resolved.
6. Generate a technical decomposition of the components of the workload. The
   technical decomposition must break down the solution into logical components.
7. Request the user to approve the generated technical decomposition.
8. If the user requests changes, then generate an updated technical
   decomposition.
9. Repeat steps 7 and 8 until the user approves the generated technical
   decomposition.
10. After the user approves the technical decomposition, proceed to Phase 2.
   **Important**: Don't proceed to the next phase until the user approves the
   generated technical decomposition of the workload.

## Phase 2: Solution architecture

In this phase, you use the requirements that were gathered in Phase 1 to
generate a detailed solution architecture for the workload or for the use case
that the user described in Phase 1.

## Ground all generated content

For each task in this phase, to ensure that the generated content aligns with
the latest and official Google Cloud guidance, you must ground the generated
content by using the following resources:
* Google Developer Knowledge MCP server
  * Server: https://developerknowledge.googleapis.com/mcp
  * Tools:
    * `developerknowledge:search_documents`
    * `developerknowledge:get_documents`
* Relevant skills from https://github.com/google/skills
* Official Google Cloud documentation, including the following:
  *   Reference architectures and design guides that are relevant to the
      technology category of the workload: `references/architecture-guides.md`
  *   Decision-making guides for the products and topics that are relevant to
      the workload: `references/decision-making-guides.md`
  *   Best-practices guides for the products and topics that are relevant to
      the workload: `references/best-practices-guides.md`

For each item in the generated guidance, you must include citations to the
relevant official Google Cloud documentation pages.

### Task 2.1: Identify Google Cloud products and features required for the workload.

1. Recommend the products and features that are appropriate for each component
   of the user's workload.

  **Important**:
  * Do not recommend any deprecated products. Verify the status of the products
    by using the resources that are listed in the
    "Ground all generated content" section.
  * Do not recommend any deprecated features. Verify the status of the features
    by using the resources that are listed in the
    "Ground all generated content" section.
  * If multiple products or features can be used for a component of the
    workload, then do the following:
    * Recommend the most appropriate product or feature. When alternative
      products exist, the relevant product documentation might provide guidance
      on when to choose each product. Follow that guidance.
    * Mention the available alternative products or features.
    * Explain the pros and cons of each alternative product or feature.

2. Present the generated product recommendations and ask the user to approve the
   recommendations.
3. If the user requests changes, then make the required changes.
4. Repeat steps 2 and 3 until the user approves the product recommendations.
5. After the user approves the product recommendations, proceed to Task 2.2.

### Task 2.2: Generate an architecture diagram.

1. Generate an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid.
2. Present the generated diagram to the user and ask the user to approve the
   architecture diagram.
3. If the user requests changes, then make the required changes.
4. Repeat steps 2 and 3 until user approves the architecture diagram.
5. After the user approves the architecture diagram, proceed to Task 2.3.

### Task 2.3: Generate an architecture description.

1. Generate a description that explains the purpose of each component, the
   relationships between the components, and the task flow or data flow.
2. Present the generated architecture description to the user and ask the user
   to approve the description.
3. If the user requests any changes, then make the required changes.
4. Repeat steps 2 and 3 until the user approves the architecture description.
5. After the user approves the architecture description, proceed to Task 2.4.

### Task 2.4: Generate design recommendations.

1. Generate design recommendations and best practices to optimally configure
   each component in the architecture based on the workload's requirements.
   **Important**:
   * When you generate design recommendations, consider the following:
     * Functional requirements that were gathered in Phase 1.
     * Non-functional requirements that were gathered in Phase 1.
   * To generate guidance for the non-functional requirements, use the following
     skills, as appropriate:
     - `google-cloud-waf-security`
     - `google-cloud-waf-reliability`
     - `google-cloud-waf-cost-optimization`
     - `google-cloud-waf-operational-excellence`
     - `google-cloud-waf-performance-optimization`
     - `google-cloud-waf-sustainability`
2. Present the generated recommendations to the user and ask whether the user
   needs any changes.
3. If the user needs changes, then make the required changes.
4. Repeat steps 2 and 3 until the user confirms that the generated design
   recommendations meet their requirements.
5. Proceed to Task 2.5.

### Task 2.5: Generate deployment guidance.

1. Generate deployment guidance, including code and instructions to enable
   the user to deploy the solution.
2. Present the generated deployment guidance to the user and ask whether the
   user needs any changes.
3. If the user requests changes, then make the required changes.
4. Repeat steps 2 and 3 until the user confirms that the generated deployment
   guidance meets their requirements.
5. Proceed to Phase 3.

## Phase 3: Solution validation

1.  Create a plan to validate the generated solution. The plan must outline the
    steps to verify that the generated solution meets the workload's
    requirements. The following are examples of validation steps:
    *   **Deployment dry-run**: Commands like `terraform plan` to preview
        the infrastructure resources that will be provisioned.
    *   **Connectivity and routing**: Verification of network paths, load
        balancer routing, and service endpoints.
    *   **Security policies**: Verification of restricted access, firewall
        rules, and IAM enforcement.
2.  Present the validation plan to the user and request feedback or approval.
3.  If the user requests changes, update the plan as required.
4.  Repeat steps 2 and 3 until the user approves the validation plan.
5.  Generate scripts or commands using tools like `curl` or `gcloud` to perform
    the steps in the approved validation plan.
6.  Request permission from the user to perform the validation checks.
7.  If the user gives permission, run the validation checks and troubleshoot any
    deployment issues.
8.  When all the validation checks pass, proceed to Phase 4.

## Phase 4: Solution packaging and presentation

In this phase, you package the generated text and code artifacts and present
the package.

1. Consolidate the text artifacts that were generated in Phase 2 and Phase 3
   into a single Markdown file named `solution-architecture-guide.md`, based on
   the template in `assets/output-template.md`.
2. Request the user's permission to write the code files in the user's
   workspace.
3. After the user gives permission, write the code files in the user's
   workspace.

## Supporting references

* https://docs.cloud.google.com/architecture/architecture-decision-records.md.txt:
  Explains when and how to use architecture decision records (ADRs) when you
  design workloads on Google Cloud.
* https://docs.cloud.google.com/architecture/deployment-archetypes.md.txt:
  Describes six Google Cloud deployment archetypes—zonal, regional,
  multi-regional, global, hybrid, and multicloud, and presents use cases and
  design considerations for each deployment archetype.
* https://docs.cloud.google.com/architecture/landing-zones.md.txt: Provides an
  overview of how to design a landing zone (also called a cloud foundation) in
  Google Cloud.
* https://docs.cloud.google.com/architecture/blueprints/security-foundations.md.txt:
  Describes best practices to deploy foundational resources, configurations, and
  capabilities to enable consistent governance, security controls, scale,
  visibility, and access to shared services across all workloads in your Google
  Cloud environment.
* https://docs.cloud.google.com/architecture/framework.md.txt: Provides
  recommendations to help architects, developers, administrators, and other
  cloud practitioners design and operate a Google Cloud topology that's secure,
  efficient, resilient, high-performing, cost-effective, and sustainable.

```


### `assets/output-template.md`

````markdown
<!-- Use this template to compile the content that you generate based on the
instructions in `SKILL.md`. -->

# Google Cloud solution architecture: [Workload Name]

## 1. Executive summary and workload overview
[A brief description of the workload, its business goals, and the high-level
solution architecture proposed.]

## 2. Requirements and current state

### 2.1. Functional requirements
* **Business processes**: [Details of the business processes supported]
* **Activities and use cases**: [Details of the key activities and use cases]

### 2.2. Non-functional requirements
* **Security**: [Details of the security requirements including compliance,
  encryption, access control requirements]
* **Reliability**: [Details of the reliability requirements including SLA,
  RTO/RPO, backup, redundancy requirements]
* **Cost**: [Details of the cost constraints and pricing models]
* **Operations**: [Details of the operational requirements including
  monitoring, logging, deployment, maintenance requirements]
* **Performance**: [Details of the performance requirements including latency,
  throughput, scaling requirements]
* **Sustainability**: [Details of the sustainability requirements including
  carbon footprint, resource optimization requirements]

### 2.3. Current state
[If applicable, describe the current on-premises or other-cloud architecture.]
* **Current infrastructure**: [Details of existing setup]
* **Pain points and drivers for migration/redesign**: [Details of the drivers
  for migration/redesign]

### 2.4. Dependencies
* **Internal dependencies**: [Details of internal dependencies including other
  workloads and internal services]
* **External dependencies**: [Details of external dependencies including
  third-party products and on-premises tools]

## 3. Technical decomposition of the workload
[Technical decomposition of the workload components, breaking down the
application into logical services or layers.]

## 4. Proposed solution architecture

### 4.1. Google Cloud products and features mapping
[Identify Google Cloud products and features mapped to the technical
components. For each component, justify the selection, note alternatives
considered, and describe the pros and cons of the recommended product/feature
and alternatives.]

| Component | Recommended Google Cloud product/feature | Justification and citations | Alternatives considered | Pros and cons of alternatives |
| :--- | :--- | :--- | :--- | :--- |
| **[Component Name]** | **[Product Name]** | [Why this product is chosen, citing official docs] | [Alternative product] | **Pros**: ... <br> **Cons**: ... |

### 4.2. Architecture diagram
[Architecture diagram in Mermaid format showing the relationships and flows
between the components of the architecture.]

```mermaid
%% Example structure
graph TD
    User([User]) --> Web[Load Balancer]
    Web --> App[Application Layer]
    App --> DB[(Database)]
```

### 4.3. Architecture description
[Detailed description of the architecture. Describe the task flow and data
flow between the components of the architecture.]
* **Data flow**: [Describe the flow of data.]
* **Tasks/control flow**: [Describe the flow of tasks/control.]

## 5. Design and configuration recommendations
[Best practices and configuration recommendations for each pillar of the
Google Cloud Architecture Framework.]

### 5.1. Security, privacy, and compliance
* **Access control**: [E.g., IAM roles, least privilege policy]
* **Data protection**: [E.g., Encryption at rest/in transit, Cloud KMS]
* **Network Security**: [E.g., VPC, Firewalls, Cloud Armor, Private Service Connect]

### 5.2. Reliability
* **Redundant deployment**: [E.g., Multi-region/regional deployment, load
balancing]
* **Backup and DR**: [E.g., Backups, failover procedures, RTO/RPO strategies]

### 5.3. Operational excellence
* **Monitoring and logging**: [E.g., Cloud Logging, Cloud Monitoring, Dashboards]
* **Infrastructure as Code (IaC)**: [E.g., Terraform, Deployment Manager]

### 5.4. Cost optimization
* **Sizing and scaling**: [E.g., Autoscaling configuration, right-sizing resources]
* **Pricing models**: [E.g., Commitment discounts, Spot VMs, flat-rate pricing]

### 5.5. Performance efficiency
* **Caching and CDN**: [E.g., Cloud CDN, Memorystore]
* **Database and query optimization**: [E.g., Partitioning, indexing, caching]

### 5.6. Sustainability
* [E.g., Serverless adoption, resource utilization, carbon footprint]

## 6. Deployment guidance
[Instructions and code for deploying the architecture.]

### 6.1. Deployment prerequisites
* [Prerequisite 1: E.g., Enabling APIs]
* [Prerequisite 2: E.g., Installing SDK/tools]
* ...and so on

### 6.2. Step-by-step deployment instructions
1. [Step 1: E.g., Authenticate with Google Cloud]
2. [Step 2: E.g., Initialize Terraform]
3. [Step 3: E.g., Apply Terraform configuration]

## 7. Validation plan
[Details of the steps to verify that the generated solution meets the workload's
 requirements. Also include references to any validation scripts that were
 generated.]

## 8. References
* [E.g., Horizontal scalability](https://docs.cloud.google.com/architecture/framework/reliability/horizontal-scalability)
* [E.g., Regional managed instance groups](https://docs.cloud.google.com/compute/docs/instance-groups/regional-migs)
````


### `references/architecture-guides.md`

```markdown
# Architecture guides

| Technology category | Brief description | Line hints |
|---------------------|-------------------|------------|
| [Fundamentals](#fundamentals) | Design guides, reference architectures, and best practices for foundational cloud architectures, landing zones, and deployment archetypes. | Lines 19-46 |
| [AI and ML](#ai-ml) | Design guides, reference architectures, and best practices for AI, MLOps, generative AI, and agentic workflows. | Lines 47-98 |
| [Application development](#application-development) | Design guides, reference architectures, and best practices for building, deploying, and running applications in the cloud. | Lines 99-132 |
| [Security, privacy, and compliance](#security-privacy-and-compliance) | Design guides, reference architectures, and best practices for security, privacy, and compliance. | Lines 133-207 |
| [Reliability and disaster recovery](#reliability-and-disaster-recovery) | Design guides, reference architectures, and best practices for reliability and disaster recovery. | Lines 208-230 |
| [Data and analytics](#data-and-analytics) | Design guides, reference architectures, and best practices for data warehousing, data mesh, data analytics, and data streaming. | Lines 231-248 |
| [Databases](#databases) | Design guides, reference architectures, and best practices for planning and implementing databases for cloud workloads. | Lines 249-268 |
| [Migration](#migration) | Methodologies, assessment frameworks, and guides for migrating VMs, databases, and applications to Google Cloud. | Lines 269-295 |
| [Networking](#networking) | Design guides, reference architectures, and best practices for planning and implementing networking for cloud workloads. | Lines 296-315 |
| [Hybrid, multi-cloud, and distributed cloud](#hybrid-multi-cloud-and-distributed-cloud) | Design guides, reference architectures, and best practices for hybrid, multi-cloud, and distributed cloud environments. | Lines 316-337 |
| [Financial services](#financial-services) | Design guides, reference architectures, and best practices for financial services workloads. | Lines 338-341 |
| [Monitoring and logging](#monitoring-and-logging) | Best practices and operational patterns for observability, log streaming, and infrastructure monitoring. | Lines 342-354 |
| [Storage](#storage) | Design guides, reference architectures, and best practices to help you plan and implement storage for cloud workloads. | Lines 355-361 |

## <a id="fundamentals"></a>Fundamentals

- [Authentication and authorization](https://docs.cloud.google.com/architecture/blueprints/security-foundations/authentication-authorization.md.txt): Introduces how to use Cloud Identity to manage identities for accessing Google Cloud services.
- [Comparative analysis of Google Cloud deployment archetypes](https://docs.cloud.google.com/architecture/deployment-archetypes/comparison.md.txt): Compares the cloud deployment archetypes in terms of the availability they provide, resilience to zone and region outages, cost, and operational complexity.
- [Decide a resource hierarchy for your Google Cloud landing zone](https://docs.cloud.google.com/architecture/landing-zones/decide-resource-hierarchy.md.txt): Provides guidance to help you decide the resource hierarchy for your Google Cloud landing zone.
- [Decide how to onboard identities to Google Cloud](https://docs.cloud.google.com/architecture/landing-zones/decide-how-to-onboard-identities.md.txt): Describes identity provisioning options for Google Cloud and the decisions that you must make when you onboard your users to Cloud Identity or Google Workspace.
- [Decide the network design for your Google Cloud landing zone](https://docs.cloud.google.com/architecture/landing-zones/decide-network-design.md.txt): Describes common network designs for landing zones, and helps you choose the option that best meets your requirements.
- [Decide the security for your Google Cloud landing zone](https://docs.cloud.google.com/architecture/landing-zones/decide-security.md.txt): Provides guidance to help you decide the security design for your Google Cloud landing zone.
- [Deploy the blueprint](https://docs.cloud.google.com/architecture/blueprints/security-foundations/summary.md.txt): Describes the process to deploy an enterprise foundation using a blueprint.
- [Deployment methodology](https://docs.cloud.google.com/architecture/blueprints/security-foundations/deployment-methodology.md.txt): Provides guidance to use declarative infrastructure to deploy an enterprise foundations blueprint.
- [Detective controls](https://docs.cloud.google.com/architecture/blueprints/security-foundations/detective-controls.md.txt): Describes the threat detection and monitoring controls that are incorporated into an enterprise foundations blueprint.
- [Enterprise foundations blueprint](https://docs.cloud.google.com/architecture/blueprints/security-foundations.md.txt): Describes a blueprint to deploy foundational resources, configurations, and capabilities to help ensure consistent governance, security, scaling, and observability for your workloads in Google Cloud.
- [Google Cloud deployment archetypes](https://docs.cloud.google.com/architecture/deployment-archetypes.md.txt): Describes the deployment archetypes that you can use for cloud applications, the use cases for each deployment archetype and the design considerations.
- [Google Cloud global deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/global.md.txt): Describes the global deployment archetype, and its use cases and design considerations.
- [Google Cloud hybrid deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/hybrid.md.txt): Describes the hybrid deployment archetype, and its use cases and design considerations.
- [Google Cloud multi-regional deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/multiregional.md.txt): Describes the multi-regional deployment archetype, and its use cases and design considerations.
- [Google Cloud multicloud deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/multicloud.md.txt): Describes the multicloud deployment archetype, and its use cases and design considerations.
- [Google Cloud regional deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/regional.md.txt): Describes the regional deployment archetype, and its use cases and design considerations.
- [Google Cloud zonal deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/zonal.md.txt): Describes the zonal deployment archetype, and its use cases and design considerations.
- [Implement your Google Cloud landing zone network design](https://docs.cloud.google.com/architecture/landing-zones/implement-network-design.md.txt): Provides steps and guidance to implement your chosen network design for your landing zone.
- [Landing zone design in Google Cloud](https://docs.cloud.google.com/architecture/landing-zones.md.txt): This series shows how to design and build a landing zone in Google Cloud, guiding you through high-level decisions about identity onboarding, resource hierarchy, network design, and security.
- [Operations best practices](https://docs.cloud.google.com/architecture/blueprints/security-foundations/operation-best-practices.md.txt): Provides best practices for deploying and operating workloads in a Google Cloud environment that&#39;s based on an enterprise foundations blueprint.
- [Organization structure](https://docs.cloud.google.com/architecture/blueprints/security-foundations/organization-structure.md.txt): Describes how the resources in an enterprise foundations blueprint are organized.
- [Preventative controls for acceptable resource configurations](https://docs.cloud.google.com/architecture/blueprints/security-foundations/preventative-controls.md.txt): Describes preventative controls that you can incorporate into an enterprise foundations blueprint.
- [Well-Architected Framework: Cost optimization pillar](https://docs.cloud.google.com/architecture/framework/cost-optimization/printable.md.txt): Provides principles and recommendations to help you optimize the cost of your workloads in Google Cloud.
- [Well-Architected Framework: Performance optimization pillar](https://docs.cloud.google.com/architecture/framework/performance-optimization/printable.md.txt): Provides principles and recommendations to help you optimize the performance of your workloads in Google Cloud.
- [Well-Architected Framework: Sustainability pillar](https://docs.cloud.google.com/architecture/framework/sustainability/printable.md.txt): Provides principles and recommendations to help you design, build, and manage workloads in Google Cloud that are energy-efficient and carbon-aware.

## <a id="ai-ml"></a>AI and ML

- [Agentic AI use case: Administer interactive learning](https://docs.cloud.google.com/architecture/agentic-ai-interactive-learning.md.txt): Design a single-agent AI system that assesses a user&#39;s knowledge on a specific topic and generates a personalized learning experience.
- [Agentic AI use case: Automate data science workflows](https://docs.cloud.google.com/architecture/agentic-ai-data-science.md.txt): Design a multi-agent AI system that automates complex data analytics and machine learning tasks.
- [Agentic AI use case: Build a multicloud open data lakehouse](https://docs.cloud.google.com/architecture/agentic-ai-build-multicloud-open-data-lakehouse.md.txt): A high-level architecture to build a multicloud open data lakehouse that establishes a highly governed, secure pipeline from raw multicloud silos to AI and agentic driven actions.
- [Agentic AI use case: Classify multimodal data](https://docs.cloud.google.com/architecture/agentic-ai-classify-multimodal-data.md.txt): A high-level architecture to design a multi-agent AI system that analyzes disparate multimodal data and produces a high-confidence classification.
- [Agentic AI use case: Enable live bidirectional multimodal streaming](https://docs.cloud.google.com/architecture/agentic-ai-bidirectional-multimodal-streaming.md.txt): A high-level architecture to build and deploy a multi-agent AI system that provides technical guidance and automated safety monitoring through a continuous, bidirectional stream of multimodal data.
- [Agentic AI use case: Multimodal GraphRAG resource orchestration](https://docs.cloud.google.com/architecture/agentic-ai-multimodal-graph-rag-resource-orchestration.md.txt): A high-level architecture to build and deploy a multi-agent AI system that consolidates fragmented multimodal data into a searchable knowledge graph.
- [Agentic AI use case: Orchestrate access to disparate enterprise systems](https://docs.cloud.google.com/architecture/agenticai-orchestrate-access-disparate-systems.md.txt): Use agentic AI to orchestrate access to disparate enterprise systems.
- [Agentic AI use case: Orchestrate security operations workflows](https://docs.cloud.google.com/architecture/agentic-ai-orchestrate-security-ops-workflows.md.txt): A high-level architecture to build a multi-agent AI system that orchestrates complex investigation and triage processes in a security operations center (SOC).
- [Architecture for MLOps using TensorFlow Extended, Agent Platform Pipelines, and Cloud Build](https://docs.cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build.md.txt): Describes the architecture of a machine learning (ML) system using TensorFlow Extended (TFX) libraries.
- [Automate utilization-review of health insurance claims using generative AI](https://docs.cloud.google.com/architecture/use-generative-ai-utilization-management.md.txt): A reference architecture for health insurance companies to automate prior authorization (PA) request processing and improve their utilization review (UR) processes.
- [Best practices for implementing machine learning on Google Cloud](https://docs.cloud.google.com/architecture/ml-on-gcp-best-practices.md.txt): Introduces best practices for implementing machine learning (ML) on Google Cloud, with a focus on custom-trained models based on your data and code.
- [Build an ML vision analytics solution with Dataflow and Cloud Vision API](https://docs.cloud.google.com/architecture/building-a-vision-analytics-solution.md.txt): How to deploy a Dataflow pipeline to process large-scale image files with Cloud Vision. Dataflow stores the results in BigQuery so that you can use them to train BigQuery ML pre-built models.
- [Build and deploy generative AI and machine learning models in an enterprise](https://docs.cloud.google.com/architecture/blueprints/genai-mlops-blueprint.md.txt): Describes the generative AI and machine learning (ML) blueprint, which deploys a pipeline for creating AI models.
- [Build trusted AI agents with Google Maps Platform](https://docs.cloud.google.com/architecture/agentic-ai-system-with-grounding-using-maps.md.txt): A high-level architecture to build trusted and effective AI agents by grounding them in real-world contextual data from Google Maps Platform and Google Calendar.
- [C3 AI architecture on Google Cloud](https://docs.cloud.google.com/architecture/partners/c3-ai-architecture.md.txt): Develop applications using C3 AI and Google Cloud.
- [Choose a design pattern for your agentic AI system](https://docs.cloud.google.com/architecture/choose-design-pattern-agentic-ai-system.md.txt): Learn how to select an agent design pattern to build your agentic system.
- [Choose your agentic AI architecture components](https://docs.cloud.google.com/architecture/choose-agentic-ai-architecture-components.md.txt): Learn how to select architecture components to build your agentic AI system.
- [Confidential computing for data analytics, AI, and federated learning](https://docs.cloud.google.com/architecture/security/confidential-computing-analytics-ai.md.txt): &#34;Learn about how you can use confidential computing in Google Cloud to encrypt data in use for confidential data analytics, AI machine learning, and federated learning.&#34;
- [Cross-silo and cross-device federated learning on Google Cloud](https://docs.cloud.google.com/architecture/cross-silo-cross-device-federated-learning-google-cloud.md.txt): Provides guidance to help you create a federated learning platform that supports either a cross-silo or cross-device architecture.
- [Delete the Generative AI document summarization](https://docs.cloud.google.com/architecture/ai-ml/generative-ai-document-summarization.md.txt): Delete the Generative AI document summarization Jump Start Solution.
- [Delete the Generative AI Knowledge Base](https://docs.cloud.google.com/architecture/ai-ml/generative-ai-knowledge-base.md.txt): Delete the Generative AI Knowledge Base Jump Start Solution.
- [Delete the Generative AI RAG with Cloud SQL](https://docs.cloud.google.com/architecture/ai-ml/generative-ai-rag.md.txt): Delete the Generative AI RAG with Cloud SQL Jump Start Solution.
- [Deploy an ML vision analytics solution with Dataflow and Cloud Vision API](https://docs.cloud.google.com/architecture/building-a-vision-analytics-solution/deployment.md.txt): Describes how to deploy a Dataflow pipeline that processes image files at scale with Cloud Vision API and then stores the processed output in BigQuery for analytics and model training.
- [Deploy and operate generative AI applications](https://docs.cloud.google.com/architecture/deploy-operate-generative-ai-applications.md.txt): Discusses techniques for building and operating generative AI applications using MLOps and DevOps principles.
- [Generative AI use case: Generate content for personalized marketing campaigns](https://docs.cloud.google.com/architecture/genai-marketing-campaigns.md.txt): Use AI to generate personalized content for targeted marketing campaigns.
- [Generative AI use case: Generate personalized product recommendations](https://docs.cloud.google.com/architecture/genai-product-recommendations.md.txt): Use AI to generate personalized product recommendations for a retail application.
- [Generative AI use case: Generate podcasts from audio files](https://docs.cloud.google.com/architecture/genai-podcasts-from-commentary.md.txt): Use AI to generate podcasts based on audio input.
- [Generative AI use case: Generate solutions for customer-support questions](https://docs.cloud.google.com/architecture/genai-customer-support.md.txt): Use AI to generate responses to support questions from customers.
- [Generative AI with RAG](https://docs.cloud.google.com/architecture/rag-reference-architectures.md.txt): Provides a list of reference architectures to deploy generative AI applications with retrieval-augmented generation (RAG) in Google Cloud.
- [GraphRAG infrastructure for generative AI using Agent Platform and Spanner Graph](https://docs.cloud.google.com/architecture/gen-ai-graphrag-spanner.md.txt): Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.
- [Guidelines for developing high-quality, predictive ML solutions](https://docs.cloud.google.com/architecture/guidelines-for-developing-high-quality-ml-solutions.md.txt): Collates some guidelines to help you assess, ensure, and control quality in machine learning (ML) solutions.
- [Harness CI/CD pipeline for RAG applications](https://docs.cloud.google.com/architecture/partners/harness-cicd-pipeline-for-rag-app.md.txt): Shows you how to implement a Harness CI/CD pipeline for a RAG-capable application in Google Cloud.
- [Implement agentic analytics workflows for distributed data](https://docs.cloud.google.com/architecture/agentic-ai-cross-cloud-analytics.md.txt): A high-level architecture for implementing cross-cloud analytics workflows that use AI agents.
- [Implement two-tower retrieval for large-scale candidate generation](https://docs.cloud.google.com/architecture/implement-two-tower-retrieval-large-scale-candidate-generation.md.txt): Learn how to implement an end-to-end two-tower candidate generation workflow with Vertex AI.
- [MLOps: Continuous delivery and automation pipelines in machine learning](https://docs.cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning.md.txt): Discusses techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems.
- [Model development and data labeling with Google Cloud and Labelbox](https://docs.cloud.google.com/architecture/partners/model-development-data-labeling-labelbox-google-cloud.md.txt): Provides guidance for building a standardized pipeline to help accelerate the development of ML models.
- [Multi-agent AI system in Google Cloud](https://docs.cloud.google.com/architecture/multiagent-ai-system.md.txt): Design robust multi-agent AI systems in Google Cloud.
- [Multi-tenant agentic AI system](https://docs.cloud.google.com/architecture/multi-tenant-agentic-ai-system.md.txt): Design a multi-tenant agentic AI system on Google Cloud.
- [Networking for AI inference model serving on all backends](https://docs.cloud.google.com/architecture/networking-for-ai-inference.md.txt): Guidance to help you create a unified frontend for multiple AI models that are hosted on-premises or by any provider, including third-party and Google Cloud.
- [Networking for AI inference model serving on GKE](https://docs.cloud.google.com/architecture/networking-for-ai-inference-gke.md.txt): Guidance to help you create a multiple-model inference service using GKE.
- [Optimize AI and ML workloads with Cloud Storage FUSE](https://docs.cloud.google.com/architecture/optimize-ai-ml-workloads-cloud-storage-fuse.md.txt): Use Cloud Storage FUSE to optimize the performance of AI and ML workloads.
- [Optimize AI and ML workloads with Google Cloud Managed Lustre](https://docs.cloud.google.com/architecture/optimize-ai-ml-workloads-managed-lustre.md.txt): Shows how to
...<truncated>
```


### `references/best-practices-guides.md`

```markdown
# Product-specific best practices guides

| Product | Line hints |
|---------|------------|
| [AI Hypercomputer](#ai-hypercomputer) | Lines 31-34 |
| [AlloyDB](#alloydb) | Lines 35-41 |
| [Backup and Disaster Recovery (DR) Service](#backup-and-disaster-recovery-dr-service) | Lines 42-49 |
| [BigQuery](#bigquery) | Lines 50-64 |
| [Cloud Armor](#cloud-armor) | Lines 65-68 |
| [Cloud Load Balancing](#cloud-load-balancing) | Lines 69-72 |
| [Cloud Run](#cloud-run) | Lines 73-80 |
| [Cloud Service Mesh](#cloud-service-mesh) | Lines 81-90 |
| [Cloud SQL](#cloud-sql) | Lines 91-98 |
| [Cloud Storage](#cloud-storage) | Lines 99-104 |
| [Compute Engine](#compute-engine) | Lines 105-121 |
| [Gemini Enterprise Agent Platform](#gemini-enterprise-agent-platform) | Lines 122-128 |
| [Google Kubernetes Engine (GKE)](#google-kubernetes-engine-gke) | Lines 129-146 |
| [Identity and Access Management (IAM)](#identity-and-access-management-iam) | Lines 147-156 |
| [Managed Service for Apache Spark](#managed-service-for-apache-spark) | Lines 157-161 |
| [Memorystore](#memorystore) | Lines 162-170 |
| [Migration](#migration) | Lines 171-175 |
| [Network Connectivity](#network-connectivity) | Lines 176-182 |
| [Observability in Google Cloud](#observability-in-google-cloud) | Lines 183-191 |
| [Pub/Sub](#pub-sub) | Lines 192-197 |
| [Security Command Center](#security-command-center) | Lines 198-201 |
| [Security](#security) | Lines 202-211 |
| [Spanner](#spanner) | Lines 212-223 |
| [Vertex AI](#vertex-ai) | Lines 224-227 |
| [Workload Manager](#workload-manager) | Lines 228-236 |

## <a id="ai-hypercomputer"></a>AI Hypercomputer

- https://docs.cloud.google.com/ai-hypercomputer/docs/networking-best-practices.md.txt

## <a id="alloydb"></a>AlloyDB

- https://docs.cloud.google.com/alloydb/docs/ai/best-practices-tuning-scann.md.txt
- https://docs.cloud.google.com/alloydb/docs/auth-proxy/best-practices.md.txt
- https://docs.cloud.google.com/alloydb/docs/import-export-best-practices.md.txt
- https://docs.cloud.google.com/alloydb/docs/security-best-practices.md.txt

## <a id="backup-and-disaster-recovery-dr-service"></a>Backup and Disaster Recovery (DR) Service

- https://docs.cloud.google.com/backup-disaster-recovery/docs/concepts/backup-plan-policy-best-practices.md.txt
- https://docs.cloud.google.com/backup-disaster-recovery/docs/concepts/best-practices-cyber-resilience.md.txt
- https://docs.cloud.google.com/backup-disaster-recovery/docs/concepts/best-practices-ransomware.md.txt
- https://docs.cloud.google.com/backup-disaster-recovery/docs/concepts/policy-specific-best-practices.md.txt
- https://docs.cloud.google.com/backup-disaster-recovery/docs/create-plan/schedule-best-practices-at-a-glance.md.txt

## <a id="bigquery"></a>BigQuery

- https://docs.cloud.google.com/bigquery/docs/best-practices-costs.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-for-multi-tenant-workloads-on-bigquery.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-performance-compute.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-performance-functions.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-performance-nested.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-performance-overview.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-policy-tags.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-row-level-security.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-spatial-analysis.md.txt
- https://docs.cloud.google.com/bigquery/docs/best-practices-storage.md.txt
- https://docs.cloud.google.com/bigquery/docs/graph-query-best-practices.md.txt
- https://docs.cloud.google.com/bigquery/docs/write-api-best-practices.md.txt

## <a id="cloud-armor"></a>Cloud Armor

- https://docs.cloud.google.com/armor/docs/best-practices.md.txt

## <a id="cloud-load-balancing"></a>Cloud Load Balancing

- https://docs.cloud.google.com/load-balancing/docs/https/http-load-balancing-best-practices.md.txt

## <a id="cloud-run"></a>Cloud Run

- https://docs.cloud.google.com/run/docs/configuring/jobs/gpu-best-practices.md.txt
- https://docs.cloud.google.com/run/docs/configuring/networking-best-practices.md.txt
- https://docs.cloud.google.com/run/docs/configuring/services/gpu-best-practices.md.txt
- https://docs.cloud.google.com/run/docs/configuring/workerpools/gpu-best-practices.md.txt
- https://docs.cloud.google.com/run/docs/tips/functions-best-practices.md.txt

## <a id="cloud-service-mesh"></a>Cloud Service Mesh

- https://docs.cloud.google.com/service-mesh/docs/canonical-service-best-practices.md.txt
- https://docs.cloud.google.com/service-mesh/docs/istio-apis/security/best-practices.md.txt
- https://docs.cloud.google.com/service-mesh/docs/istio-apis/security/egress-gateways-best-practices.md.txt
- https://docs.cloud.google.com/service-mesh/docs/operate-and-maintain/scalability-best-practices.md.txt
- https://docs.cloud.google.com/service-mesh/docs/security/anthos-service-mesh-security-best-practices.md.txt
- https://docs.cloud.google.com/service-mesh/docs/security/best-practices.md.txt
- https://docs.cloud.google.com/service-mesh/docs/security/egress-gateways-best-practices.md.txt

## <a id="cloud-sql"></a>Cloud SQL

- https://docs.cloud.google.com/sql/docs/best-practices.md.txt
- https://docs.cloud.google.com/sql/docs/mysql/best-practices.md.txt
- https://docs.cloud.google.com/sql/docs/postgres/best-practices.md.txt
- https://docs.cloud.google.com/sql/docs/postgres/manage-memory-usage-best-practices.md.txt
- https://docs.cloud.google.com/sql/docs/sqlserver/best-practices.md.txt

## <a id="cloud-storage"></a>Cloud Storage

- https://docs.cloud.google.com/storage/docs/best-practices-media-workload.md.txt
- https://docs.cloud.google.com/storage/docs/best-practices.md.txt
- https://docs.cloud.google.com/storage/docs/hns-buckets-best-practices.md.txt

## <a id="compute-engine"></a>Compute Engine

- https://docs.cloud.google.com/compute/docs/api/best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/connect/ssh-best-practices/auditing.md.txt
- https://docs.cloud.google.com/compute/docs/connect/ssh-best-practices/credentials.md.txt
- https://docs.cloud.google.com/compute/docs/connect/ssh-best-practices/index.md.txt
- https://docs.cloud.google.com/compute/docs/connect/ssh-best-practices/login-access.md.txt
- https://docs.cloud.google.com/compute/docs/connect/ssh-best-practices/network-access.md.txt
- https://docs.cloud.google.com/compute/docs/disks/snapshot-best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/images/image-families-best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/images/image-management-best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/instances/best-practices-shared-reservations.md.txt
- https://docs.cloud.google.com/compute/docs/instances/sql-server/best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/instances/windows/best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/instances/windows/windows-best-practices.md.txt
- https://docs.cloud.google.com/compute/docs/nodes/sole-tenant-best-practices.md.txt

## <a id="gemini-enterprise-agent-platform"></a>Gemini Enterprise Agent Platform

- https://docs.cloud.google.com/gemini-enterprise-agent-platform/govern/policies/best-practices.md.txt
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/capabilities/gemini-image-generation-best-practices.md.txt
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/live-api/best-practices.md.txt
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/video/best-practice.md.txt

## <a id="google-kubernetes-engine-gke"></a>Google Kubernetes Engine (GKE)

- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/batch-platform-on-gke.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/enterprise-multitenancy.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/index.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/autoscaling-tpu.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/autoscaling.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/batch-inference.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/index.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/llm-optimization.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/networking.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/optimize-ai-utilization.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/rbac.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/run-hpc.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/scalability.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/upgrading-clusters.md.txt
- https://docs.cloud.google.com/kubernetes-engine/docs/concepts/best-practices-continuous-integration-delivery-kubernetes.md.txt

## <a id="identity-and-access-management-iam"></a>Identity and Access Management (IAM)

- https://docs.cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys.md.txt
- https://docs.cloud.google.com/iam/docs/best-practices-for-using-service-accounts-in-deployment-pipelines.md.txt
- https://docs.cloud.google.com/iam/docs/best-practices-for-using-workload-identity-federation.md.txt
- https://docs.cloud.google.com/iam/docs/best-practices-service-accounts.md.txt
- https://docs.cloud.google.com/iam/docs/best-practices-workforce-identity-federation.md.txt
- https://docs.cloud.google.com/iam/docs/groups-best-practices.md.txt
- https://docs.cloud.google.com/iam/docs/pam-best-practices.md.txt

## <a id="managed-service-for-apache-spark"></a>Managed Service for Apache Spark

- https://docs.cloud.google.com/managed-spark/docs/concepts/security-best-practices.md.txt
- https://docs.cloud.google.com/managed-spark/docs/guides/best-practices.md.txt

## <a id="memorystore"></a>Memorystore

- https://docs.cloud.google.com/memorystore/docs/cluster/general-best-practices.md.txt
- https://docs.cloud.google.com/memorystore/docs/memcached/best-practices.md.txt
- https://docs.cloud.google.com/memorystore/docs/memcached/memory-management-best-practices.md.txt
- https://docs.cloud.google.com/memorystore/docs/redis/general-best-practices.md.txt
- https://docs.cloud.google.com/memorystore/docs/redis/memory-management-best-practices.md.txt
- https://docs.cloud.google.com/memorystore/docs/valkey/general-best-practices.md.txt

## <a id="migration"></a>Migration

- https://docs.cloud.google.com/migrate/containers/docs/planning-best-practices.md.txt
- https://docs.cloud.google.com/migrate/virtual-machines/docs/5.0/discover/migrating-vms-migrate-for-compute-engine-best-practices.md.txt

## <a id="network-connectivity"></a>Network Connectivity

- https://docs.cloud.google.com/network-connectivity/docs/interconnect/concepts/best-practices.md.txt
- https://docs.cloud.google.com/network-connectivity/docs/network-connectivity-center/ncc-partner/best-practices.md.txt
- https://docs.cloud.google.com/network-connectivity/docs/router/concepts/best-practices.md.txt
- https://docs.cloud.google.com/network-connectivity/docs/vpn/concepts/best-practices.md.txt

## <a id="observability-in-google-cloud"></a>Observability in Google Cloud

- https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/best-practices/config.md.txt
- https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/best-practices/ingest-and-query.md.txt
- https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/best-practices/multi-tenant.md.txt
- https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/best-practices/rule-alert-mgd.md.txt
- https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/best-practices/rule-alert-self.md.txt
- https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/best-practices/unusual-configs.md.txt

## <a id="pub-sub"></a>Pub/Sub

- https://docs.cloud.google.com/pubsub/docs/metrics-autoscaling-best-practices.md.txt
- https://docs.cloud.google.com/pubsub/docs/publish-best-practices.md.txt
- https://docs.cloud.google.com/pubsub/docs/subscribe-best-practices.md.txt

## <a id="security-command-center"></a>Security Command Center

- https://docs.cloud.google.com/security-command-center/docs/cryptomining-detection-best-practices.md.txt

## <a id="security"></a>Security

- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/agents-applications.md.txt
- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/data-management.md.txt
- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/index.md.txt
- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/infrastructure.md.txt
- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/recommended-iam-groups.md.txt
- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/secure-enterprise-foundations.md.txt
- https://docs.cloud.google.com/docs/security/security-best-practices-catalog/tools-inference.md.txt

## <a id="spanner"></a>Spanner

- https://docs.cloud.google.com/spanner/docs/best-practices-gaming-database.md.txt
- https://docs.cloud.google.com/spanner/docs/dml-best-practices.md.txt
- https://docs.cloud.google.com/spanner/docs/graph/algorithm-best-practices.md.txt
- https://docs.cloud.google.com/spanner/docs/graph/best-practices-designing-schema.md.txt
- https://docs.cloud.google.com/spanner/docs/graph/best-practices-tuning-queries.md.txt
- https://docs.cloud.google.com/spanner/docs/schema-updates-best-practices.md.txt
- https://docs.cloud.google.com/spanner/docs/sql-best-practices.md.txt
- https://docs.cloud.google.com/spanner/docs/vector-index-best-practices.md.txt
- https://docs.cloud.google.com/spanner/docs/vector-search-best-practices.md.txt

## <a id="vertex-ai"></a>Vertex AI

- https://docs.cloud.google.com/vertex-ai/docs/featurestore/best-practices.md.txt

## <a id="workload-manager"></a>Workload Manager

- https://docs.cloud.google.com/workload-manager/docs/reference/best-practices-general.md.txt
- https://docs.cloud.google.com/workload-manager/docs/reference/best-practices-mysql.md.txt
- https://docs.cloud.google.com/workload-manager/docs/reference/best-practices-openshift.md.txt
- https://docs.cloud.google.com/workload-manager/docs/reference/best-practices-redis.md.txt
- https://docs.cloud.google.com/workload-manager/docs/reference/best-practices-sap.md.txt
- https://docs.cloud.google.com/workload-manager/docs/reference/best-practices-sql-server.md.txt

```


### `references/decision-making-guides.md`

```markdown
# Decision-making guides

| Topic | Document | Description |
| :---- | :---- | :---- |
| Agentic AI: Architecture | https://docs.cloud.google.com/architecture/choose-agentic-ai-architecture-components.md.txt | Provides architectural guidance for selecting the optimal building blocks and services on Google Cloud to power your agentic AI applications. |
| Agentic AI: Design pattern | https://docs.cloud.google.com/architecture/choose-design-pattern-agentic-ai-system.md.txt | Helps you analyze your workload and select the best design pattern (single-agent, multi-agent, etc.) for constructing your generative AI system. |
| AlloyDB Omni: Deployment environment | https://docs.cloud.google.com/alloydb/omni/docs/choose-deployment.md.txt | Provides guidance on choosing the best target environment (single container, Kubernetes, or serverless) for deploying AlloyDB Omni. |
| AlloyDB: Connectivity | https://docs.cloud.google.com/alloydb/docs/choose-alloydb-connectivity.md.txt | Compares AlloyDB connectivity methods, including AlloyDB Auth Proxy, Private Service Connect (PSC), and direct VPC peering, to help you select the appropriate method. |
| AlloyDB: Machine types | https://docs.cloud.google.com/alloydb/docs/choose-machine-type.md.txt | Explains how to select the appropriate machine type for your AlloyDB primary and read instances based on vCPUs, memory, and performance. |
| AlloyDB: Vector index | https://docs.cloud.google.com/alloydb/docs/ai/choose-index-strategy.md.txt | Compares Approximate Nearest Neighbors (ANN) and K-Nearest Neighbors (KNN) indexing strategies to help you choose the appropriate model for vector search. |
| App Engine: Environment | https://docs.cloud.google.com/appengine/docs/the-appengine-environments.md.txt | Compares the App Engine Standard and Flexible environments, helping you select the appropriate environment based on language support, scaling speed, and customization requirements. |
| App Hub: Setup model | https://docs.cloud.google.com/app-hub/docs/set-up-app-hub.md.txt | Helps you select the best approach (single-project, multi-project, or organization-level) for setting up and configuring App Hub. |
| Application hosting | https://cloud.google.com/hosting-options.md.txt | Compares application hosting (compute) options: Compute Engine, GKE, Cloud Run, App Engine |
| BigQuery: ETL vs. ELT | https://docs.cloud.google.com/bigquery/docs/load-transform-export-intro.md.txt | Describes the data integration approaches to load and transform data in BigQuery using the extract, load, and transform (ELT) or the extract, transform, load (ETL) processes. |
| BigQuery: Natural language processing function | https://docs.cloud.google.com/bigquery/docs/choose-ml-text-function.md.txt | Compares the capabilities of BigQuery ML text functions, such as AI.GENERATE\_TEXT, ML.TRANSLATE, and ML.UNDERSTAND\_TEXT, to help you choose between them. |
| BigQuery: Text generation function | https://docs.cloud.google.com/bigquery/docs/choose-text-generation-function.md.txt | Compares the different text generation options in BigQuery ML (such as using Gemini models vs. other LLMs) to select the appropriate fit. |
| Bigtable: SSD vs HDD | https://docs.cloud.google.com/bigtable/docs/choosing-ssd-hdd.md.txt | Compares the performance, cost, and use cases of SSD versus HDD storage to help you select the best disk layout for your Cloud Bigtable instances. |
| CDN products: Cloud CDN vs. Media CDN | https://docs.cloud.google.com/cdn/docs/choose-cdn-product.md.txt | Helps you select the best CDN product (Cloud CDN vs. Media CDN) based on your content delivery requirements and architecture. |
| Cloud Run: When to use | https://docs.cloud.google.com/run/docs/fit-for-run.md.txt | Provides a checklist and technical assessment guide to determine if your containerized application is well-suited for deployment on Cloud Run. |
| Cloud SQL: Editions | https://docs.cloud.google.com/sql/docs/mysql/choose-edition.md.txt | Provides guidance on choosing between Enterprise and Enterprise Plus editions for Cloud SQL (applicable to MySQL, PostgreSQL, and SQL Server). |
| Cloud SQL: Machine series | https://docs.cloud.google.com/sql/docs/mysql/machine-series-overview.md.txt | Guides you in choosing the optimal machine family and series (general-purpose, memory-optimized, etc.) for your Cloud SQL instances. |
| Cloud SQL: Storage | https://docs.cloud.google.com/sql/docs/mysql/storage-options-overview.md.txt | Helps you select the best storage options (SSD vs. HDD, automatic storage increase) for Cloud SQL databases based on IOPS and throughput needs. |
| Cloud Storage: Bucket location | https://docs.cloud.google.com/storage/docs/locations.md.txt | Provides guidance to help you choose appropriate locations for Cloud Storage buckets. |
| Compute Engine: Migration path | https://docs.cloud.google.com/compute/docs/import.md.txt | Helps you evaluate and select the best migration strategy and toolset for migrating your virtual machine workloads into Compute Engine. |
| Compute Engine: Reservation type | https://docs.cloud.google.com/compute/docs/instances/choose-reservation-type.md.txt | Describes reservation types to help you decide how to reserve compute capacity for Compute Engine instances based on your workload needs. |
| Compute Engine: VM deployment strategy | https://docs.cloud.google.com/compute/docs/choose-compute-deployment-option.md.txt | Helps you select an appropriate VM deployment strategy on Compute Engine based on workload resilience, scalability, and cost requirements. |
| Compute Engine: Region selection | https://docs.cloud.google.com/solutions/best-practices-compute-engine-region-selection.md.txt | Describes the criteria that you should consider when you choose Google Cloud regions for your Compute Engine resources |
| Container runtimes | https://docs.cloud.google.com/architecture/select-managed-container-runtime-environment.md.txt | A selection guide comparing managed container options (GKE Autopilot vs. Cloud Run vs. App Engine Flexible) based on organizational and technical factors. |
| Container runtimes: GKE vs. Cloud Run | https://docs.cloud.google.com/kubernetes-engine/docs/concepts/gke-and-cloud-run.md.txt | Provides a comparison of GKE and Cloud Run and their advantages, and helps you find out if a single-platform or hybrid strategy will work for you. |
| Eventarc: Advanced vs. Standard | https://docs.cloud.google.com/eventarc/standard/docs/choose-product-edition.md.txt | Compares the features, scalability, and event-routing models of Eventarc Standard and Eventarc Advanced to help you choose the appropriate edition. |
| Firestore: Native mode vs. Datastore mode | https://docs.cloud.google.com/datastore/docs/firestore-or-datastore.md.txt | Detailed guide comparing Firestore in Native mode versus Datastore mode, including features, scaling limits, and target use cases. |
| Gemini Enterprise Agent Platform: Serverless training method | https://docs.cloud.google.com/vertex-ai/docs/training/custom-training-methods.md.txt | Helps you compare custom training options in Agent Platform, such as custom jobs, hyperparameter tuning jobs, and training pipelines. |
| Gemini Enterprise Agent Platform: Training method | https://docs.cloud.google.com/vertex-ai/docs/start/training-methods.md.txt | Guides you in selecting the appropriate model training approach in Agent Platform, comparing AutoML, custom training, and BigQuery ML. |
| Gemini Enterprise Agent Platform: Vector database for RAG Engine | https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/rag-engine/vector-db-choices.md.txt | Describes and compare the vector database options that are supported on Agent Platform RAG Engine. |
| GKE: Cluster mode | https://docs.cloud.google.com/kubernetes-engine/docs/concepts/choose-cluster-mode.md.txt | Compares GKE Autopilot and Standard modes to help you decide which level of operational control and management is best for your cluster. |
| GKE: Multi-cluster load balancing API | https://docs.cloud.google.com/kubernetes-engine/docs/concepts/choose-mc-lb-api.md.txt | Compares Multi-Cluster Gateway versus Multi-Cluster Ingress to help you choose the correct multi-cluster load balancing approach for GKE. |
| GKE: Predefined ComputeClasses for Autopilot Pods | https://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-compute-classes.md.txt | Explains GKE Autopilot ComputeClasses (Balanced, Scale-Out, Performance) to help you choose the appropriate hardware profile for your containers. |
| Google Cloud Observability: Instrumentation strategy | https://docs.cloud.google.com/stackdriver/docs/instrumentation/choose-approach.md.txt | Guides you through the process of choosing a telemetry instrumentation strategy (such as OpenTelemetry vs. legacy agents) for Google Cloud Observability. |
| Load balancing | https://docs.cloud.google.com/load-balancing/docs/choosing-load-balancer.md.txt | Helps you choose the appropriate Google Cloud load balancer based on client location, protocol, and proxy requirements. |
| Looker: Hosting | https://docs.cloud.google.com/looker/docs/choosing-hosting-option.md.txt | Provides a comprehensive guide to selecting the optimal hosting model (Looker-hosted vs. customer-hosted) based on your requirements |
| Model inference on GKE: Load balancing strategy | https://docs.cloud.google.com/kubernetes-engine/docs/concepts/machine-learning/choose-lb-strategy.md.txt | Provides guidance on load balancing strategies for AI/ML inference workloads on Google Kubernetes Engine. |
| Network Connectivity | https://docs.cloud.google.com/network-connectivity/docs/how-to/choose-product.md.txt | Provides guidance to help you choose appropriate hybrid connectivity options: Cloud Interconnect, Cloud VPN, and Network Connectivity Center. |
| Pub/Sub vs. Cloud Managed Service for Apache Kafka | https://docs.cloud.google.com/pubsub/docs/choose-pubsub-kafka.md.txt | Compares serverless global Pub/Sub versus Google Cloud's Managed Service for Apache Kafka. |
| Pub/Sub vs. Cloud Tasks | https://docs.cloud.google.com/pubsub/docs/choosing-pubsub-or-cloud-tasks.md.txt | Describes the fundamental differences (implicit vs. explicit invocation) between Pub/Sub and Cloud Tasks. |
| Pub/Sub vs. Pub/Sub Lite | https://docs.cloud.google.com/pubsub/docs/choosing-pubsub-or-lite.md.txt | Helps you choose between standard Pub/Sub and Pub/Sub Lite based on budget, operations, and scale requirements. |
| Secret Manager: Replication policy | https://docs.cloud.google.com/secret-manager/docs/choosing-replication.md.txt | Compares automatic replication versus user-managed replication policies in Secret Manager to help you decide how to secure and distribute secrets. |
| Security Command Center: Security sources | https://docs.cloud.google.com/security-command-center/docs/concepts-security-sources.md.txt | Guides you on choosing and configuring security sources (built-in, Google-provided, and third-party) to integrate with Security Command Center. |
| Storage for AI and ML | https://docs.cloud.google.com/ai-hypercomputer/docs/storage.md.txt | Introduces and compares storage services in Google Cloud that can best help you optimize GPU or TPU performance and provides recommendations on the ideal service for specific AI and ML use cases. |
| Storage strategy | https://docs.cloud.google.com/architecture/storage-advisor.md.txt | Helps you understand and choose appropriate storage services for any workload. |
| VPC: Private access | https://docs.cloud.google.com/vpc/docs/private-access-options.md.txt | Compares different private access options on Google Cloud VPC, such as Private Google Access, Private Service Connect, and serverless VPC access. |
| Workflows vs. Application Integration | https://docs.cloud.google.com/workflows/docs/choose-app-integ-or-workflows.md.txt | Compares the capabilities and ideal scenarios for Google Cloud Workflows and Application Integration to help you choose the appropriate orchestration service. |
| Workflows vs. Managed Airflow for service orchestration | https://docs.cloud.google.com/workflows/docs/choose-orchestration.md.txt | Compares Workflows versus Managed Service for Apache Airflow (Cloud Composer) based on latency, scaling models, and language (YAML vs. Python). |
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