google-cloud-solution-build-deploy-agents

Build and deploy AI agents on Google Cloud

This skill guides agents through the workflow of designing and implementing a

tailored multi-product solution in the cloud for a given workload, use case, or

requirement.

Workflow

The solution design and implementation workflow is divided into 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.

Copy this checklist into your active task/plan artifact to track progress across

the four phases:

• [ ] Phase 1: Requirements discovery and analysis completed & confirmed.
• [ ] Phase 2: Solution architecture generated & approved.
• [ ] Phase 3: Implementation plan generated & approved.
• [ ] Phase 4: Solution validation generated & approved.

Phase 1: Requirements discovery and analysis

1. Discover requirements: Gather and understand the functional and

non-functional requirements, business goals, and current state (if any) of

the workload, including its architecture, dependencies, and constraints.

Important: First, check whether the user's initial prompt has already

answered the following questions or whether the prompt explicitly asks you

to propose a solution architecture/diagram from a given set of parameters.

• If the user's prompt provides sufficient requirements and it explicitly

requests an architecture proposal or diagram, then skip asking the

questions below, and instead proceed to the step **Recommend agent

design pattern**.

• If the user's prompt doesn't provide sufficient requirements, then

complete these steps to gather missing information:

1. Ask the user to describe the functional requirements of their

workload: business processes, activities, and use cases.

1. Ask the user to describe the non-functional requirements (security,

privacy, compliance, reliability, disaster recovery, cost,

operations, performance, and sustainability) of their workloads.

1. Ask the user what existing systems, knowledge bases, product

documentation, or other documentation the AI agents need to access

for grounded guidance.

1. Ask the user to describe dependencies, if any, on other workloads,

products, or tools.

1. Review the input that the user has provided so far, and check

whether there are any ambiguities or contradictions in the input.

If you identify any ambiguities or contradictions in the

requirements that the user has provided, then do the following for

each ambiguity or contradiction that you identify:

• Describe the ambiguity or contradiction.
• Ask the user how they wish to resolve the ambiguity or

contradiction.

• If the user delegates the choice to you (e.g., the user

replies with "do what you think is best" or "you decide"),

then provide a clear suggestion to resolve the ambiguity or

contradiction, explain your reasoning, and ask the user to

approve your suggestion.

Critical: Until all the ambiguities and contradictions that you

identify are resolved according to the preceding guidance, you must

NOT recommend or generate any architecture design, technical

decomposition, or Google Cloud product recommendations.

1. Recommend agent design pattern: Evaluate the complexity, workflow,

latency, and cost requirements of the workload to recommend an agent design

pattern:

• Single-agent system: Recommend for simpler tasks, acting as an

effective starting point to refine core logic and tools.

• Multi-agent system: Recommend for complex problems requiring

multiple specialized agents to collaborate on a workflow.

1. Identify components: Based on the requirements analysis, generate a

technical decomposition of the workload. The technical decomposition must

identify the logical components of the workloads and their relationships.

Also identify any cross-cloud components, hybrid components, or on-premises

components that the solution needs to integrate with.

1. Ask for confirmation: Ask the user to confirm whether the recommended

design pattern and technical decomposition match their workload

requirements.

1. Iterate: If the user requests changes, generate an updated technical

decomposition, and ask the user to confirm the changes. Continue iterating

until the user confirms the technical decomposition. Proceed to the next

phase only after the user provides confirmation of the technical

decomposition.

Phase 2: Solution design

1. **Retrieve relevant Google Cloud guidance from

references/related-guidance.md**.

Important: Use the content that you retrieved from

references/related-guidance.md to ground the guidance that you generate in

the remaining steps of this phase.

1. Map components to Google Cloud products: For each component in the

confirmed technical decomposition, identify the appropriate Google Cloud

products and features by consulting

[product-mappings.md](references/product-mappings.md) for detailed

recommendations, trade-offs, and alternatives across networking, frontends,

agent/model runtimes, memory stores, and tools.

1. Create architecture diagram: Create an architecture diagram in Mermaid

format: https://github.com/mermaid-js/mermaid. The diagram should show the

components, their relationships, and data/control flows.

1. Generate design recommendations: Generate design guidance based on the

following Google Cloud best practices and recommendations. Use the

information in references/related-guidance.md, with an emphasis on the

guidance in references/design-principles.md.

1. Draft solution architecture: Compile the requirements, technical

decomposition, product mapping, architecture diagram, and design

recommendations into a single Markdown file adhering to the format in

[solution-template.md](assets/solution-template.md). Save this document in

the workspace as solution-architecture.md.

1. Request review: Present the generated solution architecture (including

the complete fenced mermaid code block for the diagram) directly to the

user in your response, and explicitly request their feedback or approval.

When you present the architecture, ask the user to provide approval for you

to proceed with an implementation plan.

1. Iterate: If the user requests changes, generate an updated solution

architecture and repeat the steps from "Map components to Google Cloud

products" through "Request review" until the user approves the solution

architecture.

Phase 3: Implementation plan

1. Retrieve relevant implementation resources:

Important: Use the resources in references/related-guidance.md as the

technical foundation for the Infrastructure as Code (IaC) and the deployment

instructions that you generate in the remaining steps of this phase.

1. 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

1. Generate Infrastructure as Code (IaC): Generate code (e.g., Terraform)

and deployment scripts to automate the provisioning of the proposed Google

Cloud resources.

• Where appropriate, alongside or instead of raw infrastructure scripts,

instruct the user to use Agents CLI commands (`agents-cli scaffold

create or agents-cli scaffold enhance`) to set up or enhance the

project structure, deployment configuration, and CI/CD pipelines.

1. Write deployment instructions: Draft sequential, step-by-step deployment

instructions to execute the IaC and initialize the workload components.

Compile the deployment prerequisites, IaC, and deployment instructions into

a single Markdown file adhering to the format in

[implementation-template.md](assets/implementation-template.md). Save this

document in the workspace as implementation-instructions.md.

• The instructions MUST provide the exact ADK code to define a stateful

agent node that takes a prompt, calls a model, and returns a tool

execution request.

• The instructions MUST demonstrate how to register tools like database

readers by using Model Context Protocol (MCP) standards.

• If deploying the agent to Cloud Run, the instructions MUST show how to

configure Cloud Run to scale to zero when the agent is idle, reducing

runtime costs.

• The instructions MUST recommend using encrypted environment variables to

store model parameters or private API credentials. Encryption helps to

prevent the exposure of sensitive credentials in plain-text container

log streams.

• Where appropriate, the instructions MUST specify using the Agents CLI

agents-cli deploy command (alongside or instead of raw

infrastructure/deployment scripts) to run the deployment.

1. Request review: Present the generated deployment instructions to the

user and explicitly request their feedback and confirmation.

1. Iterate: If the user requests changes, generate an updated

implementation plan and repeat the steps from "Generate Infrastructure as

Code (IaC)" through "Request review" until the user approves the

implementation plan.

Phase 4: Solution validation

1. Retrieve relevant verification resources:

Important: Use the resources in references/related-guidance.md and their

verification patterns as the starting point for the validation checks and

verification scripts that you generate in the remaining steps of this phase.

1. 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.

1. 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).

1. Compile validation plan: Document the validation steps, verification

scripts, and expected outcomes in a single Markdown file adhering to the

format in [validation-template.md](assets/validation-template.md). Save this

document in the workspace as validation-plan.md.

1. Request review: Present the validation plan to the user and explicitly

request their feedback or approval on the validation plan.

1. Conduct validation and finalize: Assist the user in executing the

validation checks and troubleshooting any deployment issues. After the

solution is validated successfully, request final approval from the user.

1. Iterate: If the user requests changes, generate an updated validation

plan and repeat the steps from "Define validation checks" through "Request

review" until the user approves the validation plan.

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References & Supporting Links

• For the complete list of Google Cloud architectural documentation, product

manuals, development kits, and checklists used by this skill, see

[related-guidance.md](references/related-guidance.md).