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

name	google-cloud-solution-agentic-ai-bidirectional-streaming
metadata	{"category":"AiAndMachineLearning"}
description	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.

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.

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.