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

SKILL.md

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
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:

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

Agents CLI reference

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.

Use Agents CLI commands (agents-cli scaffold create or agents-cli scaffold enhance) to set up the agent application
structure, and deploy the agent to Cloud Run.

 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.

Use the Agents CLI agents-cli deploy command (alongside or instead of
raw infrastructure/deployment scripts) to run the agent deployment.

 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. Include instructions to run agent deployment in dry-run mode
(e.g., using agents-cli deploy --dry-run or -n) to preview steps and
Terraform executions before pushing to production.

Local testing and quality verification: Recommend using the Agents
CLI to run and test agent logic locally (agents-cli run) and conduct
systematic evaluations (agents-cli eval run) to verify agent quality
and performance before deploying.

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, gcloud, or agents-cli)
that the user can run to perform these validation checks.

The validation plan MUST include instructions using the Agents CLI for
local runs, evaluations, and post-deployment validation checks (e.g.,
agents-cli run --url <service-url> to test the deployed service
endpoint).

 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.