Data science workflow with AI agents solution

基于AI Agent的数据科学工作流解决方案

This skill guides agents through the workflow to design and implement a tailored multi-product solution in the cloud for a given workload, use case, or requirement.

本技能指导智能体完成云环境下针对特定工作负载、用例或需求的定制化多产品解决方案的设计与实施流程。

Workflow

工作流

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、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，为工作负载构建技术栈、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落地解决方案。
阶段4：解决方案验证：验证部署是否满足工作负载的需求。

Product Renaming & Terminology

产品更名与术语说明

When generating solution designs, architecture diagrams, and documentation, check the latest Google Cloud documentation for the most up-to-date product names. The table below provides examples of name mappings to be aware of. Note that underlying APIs, Terraform resources, and IAM roles may retain their legacy identifiers.

Legacy Name	Updated Name
Vertex AI	Gemini Enterprise Agent Platform
Vertex AI Agent Engine	Gemini Enterprise Agent Runtime

在生成解决方案设计、架构图及文档时，请查阅最新的Google Cloud文档以获取最新的产品名称。下表提供了需要注意的名称映射示例。请注意，底层API、Terraform资源及IAM角色可能仍保留旧版标识符。

旧名称	更新后名称
Vertex AI	Gemini Enterprise Agent Platform
Vertex AI Agent Engine	Gemini Enterprise Agent Runtime

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 by asking clarifying questions. You must halt and wait for the user to answer these questions before proceeding to the Identify components step. Use the following questions to guide this requirements discovery process:
- What data sources and data types do you need to access and analyze?
- Who are the target end users, and what network access model do you require?
- What types of user queries or analytical requests do you expect end users to submit to the system?
- What performance, security, or governance constraints apply?
Step 2: Identify components: Only after the user has responded to the clarifying questions in the Discover requirements step, analyze their responses to identify the components of the workload and their relationships. Also identify any cross-cloud, hybrid, or on-premises components that the solution needs to integrate with.
Step 3: Generate component decomposition: Generate a technical decomposition outlining the technical components of the workload and their relationships.
Step 4: Ask for confirmation: Present the technical decomposition and ask the user to confirm if it matches their workload requirements. Do not proceed to Phase 2 until this is confirmed.
Step 5: Iterate: If the user requests changes, generate an updated technical decomposition and ask for confirmation again. Continue iterating until the user explicitly confirms the decomposition.

步骤1：发现需求：通过提出澄清问题，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）。在进入识别组件步骤前，必须暂停并等待用户回答这些问题。使用以下问题引导需求发现过程：
- 需要访问和分析哪些数据源与数据类型？
- 目标终端用户是谁，需要采用何种网络访问模型？
- 预期终端用户会向系统提交哪些类型的查询或分析请求？
- 存在哪些性能、安全或治理约束？
步骤2：识别组件：仅在用户回答完发现需求步骤中的澄清问题后，分析其回复以识别工作负载的组件及其关系。同时识别解决方案需要集成的任何跨云、混合云或本地组件。
步骤3：生成组件分解：生成技术分解文档，概述工作负载的技术组件及其关系。
步骤4：请求确认：展示技术分解文档，并询问用户是否符合其工作负载需求。在获得确认前，请勿进入阶段2。
步骤5：迭代优化：如果用户要求修改，生成更新后的技术分解文档并再次请求确认。持续迭代直到用户明确确认分解文档。

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.
Step 2: Define agentic AI design pattern: Select the appropriate agent design pattern and agent breakdown based on the workload requirements:
- Recommended primary pattern: Coordinator pattern.
- Alternative patterns:
  - Single-agent pattern: For simpler workloads scoped to a single data source and direct tool use without multi-agent orchestration overhead.
  - Sequential or parallel pattern: For deterministic data processing pipelines with predefined, non-adaptive execution steps or concurrent data gathering.
  - Review and critique pattern: For complex or high-stakes data science tasks that require dedicated critic loops.
Step 3: Map components to Google Cloud products: For each component in the confirmed technical decomposition and agentic design pattern, identify the appropriate Google Cloud products and features, based on the guidelines in /references/product-mapping.md.
Step 4: 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 use component labels and groupings consistent with the official Google Cloud architecture icons.
Step 5: Generate design recommendations: Generate design guidance based on the guidelines in /references/design-recommendations.md.
Step 6: 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 7: Request review: Present the generated solution architecture to the user and request their feedback or approval. You must halt and wait for the user's explicit approval before proceeding to Phase 3.
Step 8: Iterate: If the user requests changes, then generate an updated solution architecture and repeat steps 2-7 in this phase until the user explicitly approves the solution architecture.

步骤1：获取相关Google Cloud文档：在进入本阶段后续步骤前，使用可用的搜索或获取工具阅读以下Google Cloud文档内容，为生成的指导提供依据。
步骤2：定义智能体AI设计模式：根据工作负载需求选择合适的Agent设计模式及Agent拆分方式：
- 推荐主模式：协调者模式。
- 备选模式：
  - 单Agent模式：适用于限定单一数据源、直接使用工具且无需多Agent编排开销的简单工作负载。
  - 顺序或并行模式：适用于具有预定义、非自适应执行步骤或并发数据收集的确定性数据处理管道。
  - 审核与评审模式：适用于需要专用评审循环的复杂或高风险数据科学任务。
步骤3：将组件映射到Google Cloud产品：根据/references/product-mapping.md中的指南，为已确认的技术分解及智能体设计模式中的每个组件选择合适的Google Cloud产品与功能。
步骤4：创建架构图：创建架构图，展示组件、组件间关系及数据/控制流。
- 图必须采用Mermaid格式：https://github.com/mermaid-js/mermaid。
- 图必须使用与官方Google Cloud架构图标一致的组件标签与分组。
步骤5：生成设计建议：根据/references/design-recommendations.md中的指南生成设计指导。
步骤6：起草解决方案架构文档：将需求、技术分解、产品映射、架构图及设计建议整合到一个名为
```
solution-architecture-guide.md
```
的Markdown文件中，基于
```
/assets/output-template.md
```
中的模板。
步骤7：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。在获得用户明确批准前，请勿进入阶段3。
步骤8：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，并重复本阶段的步骤2-7，直到用户明确批准解决方案架构。

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, such as 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. Update deployment instructions in
```
solution-architecture-guide.md
```
, based on the template in
```
assets/output-template.md
```
.
Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation. You must halt and wait for the user's explicit approval before proceeding to Phase 4.
Step 6: Iterate: If the user requests changes, then repeat steps 2-5 to generate an updated implementation plan that the user requested.
Step 7: Proceed to the next phase: After the user approves the implementation plan, proceed to Phase 4.

步骤1：获取相关实施资源：
重要提示：将这些资源作为本阶段后续步骤中生成基础设施即代码（IaC）及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：
- 项目与计费关联
- 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）及部署脚本，以自动化配置拟议的Google Cloud资源。
步骤4：编写部署说明：起草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。基于
```
assets/output-template.md
```
中的模板，更新
```
solution-architecture-guide.md
```
中的部署说明。
步骤5：请求评审：向用户展示生成的部署说明以获取反馈与确认。在获得用户明确批准前，请勿进入阶段4。
步骤6：迭代优化：如果用户要求修改，则重复步骤2-5以生成符合用户要求的更新版实施计划。
步骤7：进入下一阶段：用户批准实施计划后，进入阶段4。

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's 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 you validate the solution successfully, request final approval from the user.
Step 6: Iterate: If the user requests changes, then generate an updated validation plan and repeat the validation drafting and script generation steps in this phase until the user approves the validation plan.

步骤1：获取相关验证资源（可选）：如果阶段3的资源不在当前上下文范围内，获取相同的实施资源作为本阶段生成验证检查与验证脚本的起点。
步骤2：定义验证检查：概述验证步骤以确认部署的基础设施满足工作负载需求：
- 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、负载均衡器路由及服务端点。
- 安全策略：验证受限访问、防火墙规则及IAM执行情况。
步骤3：生成验证脚本：起草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、验证脚本及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。成功验证解决方案后，请求用户最终批准。
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，并重复本阶段的验证起草与脚本生成步骤，直到用户批准验证计划。

google-cloud-solution-agentic-ai-data-science-workflow

Original

Translation