/google-cloud-solution-build-deploy-agents

name: google-cloud-solution-build-deploy-agents
metadata:
  category: MultiProductSolutions
description: >-
  Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud.
  Provides an interactive workflow to gather requirements, recommend a tailored
  architecture, and generate deployment instructions. Use when designing or
  implementing agentic systems on Google Cloud. Don't use for general Google
  Cloud solution architecture (use google-cloud-solution-architecture instead)
  or for narrow tasks targeting a single product without agent context.

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.

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

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

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

5. 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.

2. **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.

3. **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.

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

5. **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`**.