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

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: google-cloud-solution-agentic-ai-bidirectional-streaming
metadata:
  category: MultiProductSolutions
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](https://docs.cloud.google.com/architecture/agentic-ai-bidirectional-multimodal-streaming.md.txt)
   - [Multi-agent AI system in Google Cloud](https://docs.cloud.google.com/architecture/multiagent-ai-system.md.txt)
   - [Choose your agentic AI architecture components](https://docs.cloud.google.com/architecture/choose-agentic-ai-architecture-components.md.txt)
   - [Multi-agent private networking patterns in Go