google-cloud-solution-agentic-ai-bidirectional-streaming

Live bidirectional multimodal streaming agentic AI solution

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.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution.

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.

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, including its architecture, dependencies, and constraints. Use the following questions to guide the requirements discovery process:

  • What are the primary input modalities (audio, video, or text) and what is the target latency for real-time, narrated feedback?
  • Do you require real-time safety monitoring, hazard detection, or visual inspection? If so, then what specific safety hazards, operational risks, or incorrect steps need to be monitored and detected in the video stream?
  • What existing systems, knowledge bases, product documentation, or schematic repositories must the AI agents access for grounded guidance?
  • What are the client-side device constraints and network limitations?

• [ ] 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.

Phase 2: Solution design

• [ ] Step 1: Retrieve relevant Google Cloud documentation:

  • Enable live bidirectional multimodal streaming
  • Multi-agent AI system in Google Cloud
  • Choose your agentic AI architecture components
  • Multi-agent private networking patterns in Google Cloud

  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.

Phase 3: Implementation plan

• [ ] Step 1: Retrieve relevant implementation resources:

  • Host AI agents on Cloud Run
  • Triggering Cloud Run with WebSockets
  • Start and Manage a Gemini Live API Session
  • ADK Streaming Tools
  • ADK Streaming Configuration
  • Codelab: Way Back Home Level 4 instructions (and solution code)

  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, 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.

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 requirements:

  • Deployment dry-run: Commands like terraform plan to preview changes.
  • 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, 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.