Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.
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Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
安装前可以查看交给 AI 代理的指令内容。
This skill guides agents through the workflow to design and implement a tailored multi-product solution in the cloud for a live, bidirectional multimodal streaming workload, use case, or requirement.
The solution design and implementation workflow consists of the following phases:
Step 1: Discover requirements: Understand the functional and non-functional requirements, business goals, and current state (if any) of the workload, including its architecture, dependencies, and constraints. Use the following questions to guide the requirements discovery process:
Step 2: Identify components: Based on the requirements analysis, identify the components of the workload and their relationships. Also identify any cross-cloud components, hybrid components, or on-prem components that the solution needs to integrate with.
Step 3: Generate component decomposition: Generate a technical decomposition of the components of the workload. The technical decomposition must break down the solution into logical components.
Step 4: Ask for confirmation: Ask the user to confirm whether the generated technical decomposition matches their workload requirements.
Step 5: Iterate: If the user requests changes, then generate an updated technical decomposition, and ask the user to confirm the changes. Continue iterating until the user confirms the technical decomposition.
Step 1: Retrieve relevant Google Cloud documentation:
Important: Use the content that you retrieve from Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase.
Step 2: 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 3: Create architecture diagram: Generate an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid.
Step 4: Generate design recommendations: Generate design guidance based on the guidelines in references/design-recommendations.md.
Step 5: 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 6: Request review: Present the generated solution architecture to the user and request their feedback or approval.
Step 7: Iterate: If the user requests changes, generate an updated solution architecture and repeat steps 2-6 until the user approves the solution architecture.
Step 1: Retrieve relevant implementation resources:
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:
Step 3: Generate Infrastructure as Code (IaC): Generate code, like 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.
Step 6: Iterate: If the user requests changes, then generate an updated implementation plan and repeat steps 2-5 until the user approves the implementation plan.
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 requirements:
terraform plan to preview
changes. Step 3: Generate verification scripts: Draft lightweight scripts or
command-line instructions, such as 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
solution-architecture-guide.md, based on the template in
assets/output-template.md.
Step 5: 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.
Step 6: Iterate: If the user requests changes, then generate an updated validation plan and repeat steps 2-5 until the user approves the validation plan.
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Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
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Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
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Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.
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Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.
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Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.
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Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.
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