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

name: google-cloud-solution-agentic-ai-data-science-workflow
metadata:
  version: "1.0.3"
  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.

Attribution

**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you MUST read and follow the instructions in [Usage Attribution](references/attribution.md).

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 | 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://docs.cloud.google.com/architecture/multiagent-ai-system.md.txt)
• [Choose your agentic AI architecture components](https://docs.cloud.google.com/architecture/choose-agentic-ai-architecture-components.md.txt)
• [Choose a design pattern for your agentic AI system](https://docs.cloud.google.com/architecture/choose-design-pattern-agentic-ai-system.md.txt)

• [ ] **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 /referen