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

name	google-cloud-solution-agentic-ai-data-science-workflow
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
  • 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.

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.