google-cloud-solution-build-deploy-agents

SKILL.md

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

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:

Ask the user to describe the functional requirements of their
workload: business processes, activities, and use cases.

Ask the user to describe the non-functional requirements (security,
privacy, compliance, reliability, disaster recovery, cost,
operations, performance, and sustainability) of their workloads.

Ask the user what existing systems, knowledge bases, product
documentation, or other documentation the AI agents need to access
for grounded guidance.

Ask the user to describe dependencies, if any, on other workloads,
products, or tools.

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.

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.

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.

Ask for confirmation: Ask the user to confirm whether the recommended
design pattern and technical decomposition match their workload
requirements.

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

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.

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 for detailed
recommendations, trade-offs, and alternatives across networking, frontends,
agent/model runtimes, memory stores, and tools.

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.

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.

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. Save this document in
the workspace as solution-architecture.md.

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.

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

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.

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

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.

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. 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.

Request review: Present the generated deployment instructions to the
user and explicitly request their feedback and confirmation.

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

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.

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.

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).

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. Save this
document in the workspace as validation-plan.md.

Request review: Present the validation plan to the user and explicitly
request their feedback or approval on the validation plan.

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.

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.

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.