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.
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:
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 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.
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.
