Google Cloud Solution Agentic AI Data Science Workflow Agent Skill

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
• Multi-agent AI system in Google Cloud
• Choose your agentic AI architecture components
• Choose a design pattern for your agentic AI system

• [ ] 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.

Phase 3: Implementation plan

• [ ] Step 1: Retrieve relevant implementation resources:
• ADK Data Science Sample Code
• Stateful Data Science Agent on Agent Engine
• Build and deploy an AI agent to Cloud Run using ADK
• Use AlloyDB with agents
• MCP Toolbox for Databases Configuration

_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.

Phase 4: Solution validation

• [ ] 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.