Borderless open data lakehouse agentic AI system

无边界开放数据湖仓Agentic AI系统

Follow this workflow to help users design and implement a custom multi-product solution in the cloud for a given workload, use case, or requirement.

按照此工作流程，帮助用户针对特定工作负载、用例或需求，在云端设计并实现定制化多产品解决方案。

Product Renaming & Terminology

产品重命名与术语规范

When generating solution designs, architecture diagrams, and documentation, use the updated Google Cloud product names. For details on legacy vs. updated product names and terminology, see references/product_renaming.md.

在生成解决方案设计、架构图和文档时，请使用更新后的Google Cloud产品名称。有关旧版与更新版产品名称及术语的详细信息，请参阅references/product_renaming.md。

Workflow

工作流程

The solution design and implementation workflow consists of the following phases:

Phase 1: Requirements discovery and analysis: Analyze the workload's requirements, constraints, dependencies, and current state.
Phase 2: Solution design: Build a technology stack, architecture, and deployment configuration for the workload based on Google Cloud design best practices and recommendations.
Phase 3: Implementation plan: Generate automation and instructions to deploy the solution.
Phase 4: Solution validation: Validate that the deployment meets the requirements of the workload.

解决方案设计与实施工作流程包含以下阶段：

阶段1：需求发现与分析：分析工作负载的需求、约束条件、依赖关系及当前状态。
阶段2：解决方案设计：基于Google Cloud设计最佳实践与建议，构建适用于该工作负载的技术栈、架构及部署配置。
阶段3：实施计划：生成自动化脚本与部署说明，以部署解决方案。
阶段4：解决方案验证：验证部署是否满足工作负载的需求。

Phase 1: Requirements discovery and analysis

阶段1：需求发现与分析

Step 1: Discover requirements: Understand the functional and non-functional requirements, business goals, and current state (if any) of the workload, including its architecture, dependencies, and constraints. Use the following questions to guide the requirements discovery process:
- What are your primary data sources?
- How do you manage and federate metadata across your data sources?
- What are your security and credential management requirements?
- What are the analytical and computational requirements to join and transform this borderless data?
- What types of natural language prompts or user queries do you expect AI agents or end-users to execute against this data?
Step 2: Identify components: Based on the requirements analysis, identify the components of the workload and their relationships. Also identify any borderless components, hybrid components, or on-prem components that the solution needs to integrate with.
Step 3: Generate component decomposition: Generate a technical decomposition of the components of the workload.
Step 4: Ask for confirmation: Ask the user to confirm whether the generated technical decomposition matches their workload requirements.
Step 5: Iterate: If the user requests changes, then generate an updated technical decomposition, and ask the user to confirm the changes. Continue iterating until the user confirms the technical decomposition.

步骤1：发现需求：了解工作负载的功能与非功能需求、业务目标及当前状态（如有），包括其架构、依赖关系和约束条件。使用以下问题引导需求发现过程：
- 您的主要数据源有哪些？
- 您如何跨数据源管理和联合元数据？
- 您的安全与凭证管理需求是什么？
- 整合与转换这些无边界数据需要哪些分析和计算能力？
- 您期望AI Agent或最终用户针对这些数据执行哪些类型的自然语言提示或用户查询？
步骤2：识别组件：基于需求分析，识别工作负载的组件及其相互关系。同时识别解决方案需要集成的任何无边界组件、混合组件或本地部署组件。
步骤3：生成组件分解：生成工作负载组件的技术分解文档。
步骤4：请求确认：请用户确认生成的技术分解是否符合其工作负载需求。
步骤5：迭代优化：如果用户要求修改，则生成更新后的技术分解，并请用户确认修改内容。持续迭代直至用户确认技术分解无误。

Phase 2: Solution design

阶段2：解决方案设计

Step 1: Retrieve relevant Google Cloud documentation: Use available search or fetch tools to read the content of the following Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase before proceeding.
Important: Use the content that you retrieve from Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase.
Step 2: Map components to Google Cloud products: For each component in the confirmed technical decomposition, identify the appropriate Google Cloud products and features, based on the guidelines in references/product_mapping.md.
Step 3: Create architecture diagram: Create an architecture diagram that shows the components, their relationships, and data/control flows.
- The diagram must be in the Mermaid format: https://github.com/mermaid-js/mermaid.
- The diagram must show a clear distinction between the products in the data ingestion subsystem and the serving subsystem.
- The diagram must show Managed Service for Apache Spark as a shared component, bridging the data ingestion and serving subsystems.
Step 4: Generate design recommendations: Generate design guidance based on the guidelines in references/design_recommendations.md.
Step 5: Draft solution architecture: Compile the requirements, technical decomposition, product mapping, architecture diagram, and design recommendations into a single Markdown file named
```
solution-architecture-guide.md
```
, based on the template in assets/output-template.md.
Step 6: Request review: Present the generated solution architecture to the user and request their feedback or approval.
Step 7: Iterate: If the user requests changes, generate an updated solution architecture and repeat steps 2-6 until the user approves the solution architecture.

步骤1：检索相关Google Cloud文档：使用可用的搜索或获取工具，阅读以下Google Cloud文档内容，为该阶段后续步骤生成的指导内容提供依据，然后再继续推进。
重要提示：使用从Google Cloud文档中检索到的内容，为该阶段后续步骤生成的指导内容提供依据。
步骤2：将组件映射到Google Cloud产品：针对已确认的技术分解中的每个组件，根据references/product_mapping.md中的指南，识别合适的Google Cloud产品及功能。
步骤3：创建架构图：创建展示组件、组件间关系及数据/控制流的架构图。
- 架构图必须采用Mermaid格式：https://github.com/mermaid-js/mermaid。
- 架构图必须清晰区分数据摄入子系统和服务子系统中的产品。
- 架构图必须将Managed Service for Apache Spark作为共享组件，连接数据摄入子系统和服务子系统。
步骤4：生成设计建议：基于references/design_recommendations.md中的指南生成设计指导内容。
步骤5：草拟解决方案架构：将需求、技术分解、产品映射、架构图和设计建议整合到一个名为
```
solution-architecture-guide.md
```
的Markdown文件中，参考assets/output-template.md中的模板。
步骤6：请求评审：向用户展示生成的解决方案架构，并请求反馈或批准。
步骤7：迭代优化：如果用户要求修改，则生成更新后的解决方案架构，并重复步骤2-6，直至用户批准解决方案架构。

Phase 3: Implementation plan

阶段3：实施计划

Step 1: Retrieve relevant implementation resources:
Important: Use these resources as the technical foundation for the IaC and deployment instructions you generate in the remaining steps of this phase.
Step 2: Identify deployment prerequisites: Document prerequisites for the deployment, including the following:
- Projects and billing associations
- Required Google Cloud APIs
- Required IAM permissions
- Any other prerequisites
Step 3: Generate Infrastructure as Code (IaC): Generate code (e.g., Terraform) and deployment scripts to automate the provisioning of the proposed Google Cloud resources.
Step 4: Write deployment instructions: Draft sequential, step-by-step deployment instructions to execute the IaC and initialize the workload components.
Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation.
Step 6: Iterate: If the user requests changes, generate an updated implementation plan and repeat steps 2-5 until the user approves the implementation plan.

步骤1：检索相关实施资源：
重要提示：将这些资源作为生成该阶段后续步骤中的基础设施即代码（IaC）和部署说明的技术基础。
步骤2：识别部署先决条件：记录部署的先决条件，包括以下内容：
- 项目与账单关联
- 所需的Google Cloud API
- 所需的IAM权限
- 其他任何先决条件
步骤3：生成基础设施即代码（IaC）：生成代码（如Terraform）和部署脚本，以自动化部署提议的Google Cloud资源。
步骤4：编写部署说明：草拟按顺序排列的分步部署说明，以执行IaC并初始化工作负载组件。
步骤5：请求评审：向用户展示生成的部署说明，以获取反馈和确认。
步骤6：迭代优化：如果用户要求修改，则生成更新后的实施计划，并重复步骤2-5，直至用户批准实施计划。

Phase 4: Solution validation

阶段4：解决方案验证

Step 1: Retrieve relevant verification resources (optional): If the resources from Phase 3 are not already in your context, retrieve the same implementation resources as the starting point for the validation checks and verification scripts that you generate in this phase.
Step 2: Define validation checks: Outline validation steps to verify that the deployed infrastructure meets the workload requirements:
- Deployment dry-run: Commands like
```
terraform plan
```
  to preview changes.
- Connectivity and routing: Verification of network paths, load balancer routing, and service endpoints.
- Security policies: Verification of restricted access, firewall rules, and IAM enforcement.
Step 3: Generate verification scripts: Draft lightweight scripts or command-line instructions (e.g. using
```
curl
```
or
```
gcloud
```
) that the user can run to perform these validation checks.
Step 4: Compile validation report: Document the validation steps, verification scripts, and expected outcomes in a single Markdown file.
Step 5: Conduct validation and finalize: Assist the user in executing the validation checks and troubleshooting any deployment issues. After the solution is validated successfully, request final approval from the user.
Step 6: Iterate: If the user requests changes, then generate an updated validation plan and repeat steps 2-5 until the user approves the validation plan.

步骤1：检索相关验证资源（可选）：如果阶段3的资源尚未在您的上下文环境中，请检索相同的实施资源，作为该阶段生成验证检查和验证脚本的起点。
步骤2：定义验证检查：概述验证步骤，以验证已部署的基础设施是否满足工作负载需求：
- 部署预演：使用
```
terraform plan
```
  等命令预览变更。
- 连通性与路由：验证网络路径、负载均衡器路由和服务端点。
- 安全策略：验证受限访问、防火墙规则和IAM执行情况。
步骤3：生成验证脚本：草拟轻量级脚本或命令行说明（如使用
```
curl
```
或
```
gcloud
```
），供用户运行以执行这些验证检查。
步骤4：编制验证报告：将验证步骤、验证脚本和预期结果记录在单个Markdown文件中。
步骤5：执行验证并最终确定：协助用户执行验证检查，并排查任何部署问题。解决方案成功验证后，请用户最终批准。
步骤6：迭代优化：如果用户要求修改，则生成更新后的验证计划，并重复步骤2-5，直至用户批准验证计划。

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