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

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: google-cloud-solution-agentic-ai-data-science-workflow
metadata:
  category: MultiProductSolutions
description: >-
  Designs a tailored multi-product agentic data science
  architecture on Google Cloud that incorporates opinionated best practices. Use
  when architecting multi-product solutions for agent-based data analytics or ML
  workloads. Don't use for simple queries, non-agentic pipelines, general cloud
  reviews, or writing agent code.
---

# Data science workflow with AI agents solution

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.

## Workflow

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.

## Product Renaming & Terminology

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 |

### Phase 1: Requirements discovery and analysis

- [ ] **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.

### Phase 2: Solution design

- [ ] **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.
   - [Data science workflow with AI agents](https://docs.cloud.google.com/architecture/agentic-ai-data-science.md.txt)
   - [Multi-agent AI system in Google Cloud](https://doc