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

Google Cloud solution architecture: Live bidirectional multimodal streaming agentic AI solution

1. Executive summary and workload overview

[A brief description of the workload, its business goals, and the high-level solution architecture proposed.]

2. Requirements and current state

2.1. Functional requirements

• Business processes: [Details of the business processes supported]
• Activities and use cases: [Details of the key activities and use cases]

2.2. Non-functional requirements

• Security: [Details of the security requirements including compliance, encryption, access control requirements]
• Reliability: [Details of the reliability requirements including SLA, RTO/RPO, backup, redundancy requirements]
• Cost: [Details of the cost constraints and pricing models]
• Operations: [Details of the operational requirements including monitoring, logging, deployment, maintenance requirements]
• Performance: [Details of the performance requirements including latency, throughput, scaling requirements]
• Sustainability: [Details of the sustainability requirements including carbon footprint, resource optimization requirements]

2.3. Current state

[If applicable, describe the current on-premises or other-cloud architecture.]

• Current infrastructure: [Details of existing setup]
• Pain points and drivers for migration/redesign: [Details of the drivers for migration/redesign]

2.4. Dependencies

• Internal dependencies: [Details of internal dependencies including other workloads and internal services]
• External dependencies: [Details of external dependencies including third-party products and on-premises tools]

3. Technical decomposition of the workload

[Technical decomposition of the workload components, breaking down the application into logical services or layers.]

4. Proposed solution architecture

4.1. Google Cloud products and features mapping

[Identify Google Cloud products and features mapped to the technical components. For each component, justify the selection, note alternatives considered, and describe the pros and cons of the recommended product/feature and alternatives.]

4.2. Architecture diagram

[Architecture diagram in Mermaid format showing the relationships and flows between the components of the architecture.]

%% Example structure
graph TD
    User([User]) --> Web[Load Balancer]
    Web --> App[Application Layer]
    App --> DB[(Database)]

4.3. Architecture description

[Detailed description of the architecture. Describe the task flow and data flow between the components of the architecture.]

• Data flow: [Describe the flow of data.]
• Tasks/control flow: [Describe the flow of tasks/control.]

5. Design and configuration recommendations

[Best practices and configuration recommendations for each pillar of the Google Cloud Architecture Framework.]

5.1. Security, privacy, and compliance

• Access control: [E.g., Disable default run.app URL, configure regional external Application Load Balancer with Cloud Armor for request filtering, rate limiting, and DDoS protection]
• Data protection: [E.g., Enforce TLS encryption for bidirectional WebSocket connections to protect sensitive audio/video data, IAM least privilege policies]
• Agent-to-Agent Security: [E.g., Extended agent cards with OIDC identity tokens for Agent2Agent (A2A) authentication]
• Human oversight: [E.g., Human-in-the-loop flows to let supervisors monitor, pause, and override business-critical agent actions]

5.2. Reliability

• Agent architecture: [E.g., Fault-tolerant agents with decentralized designs]
• Staging and validation: [E.g., Simulate inter-agent coordination issues in replica staging environment]
• High availability and quota: [E.g., Regional multi-zone Cloud Run deployment, Provisioned Throughput for critical production model workloads]

5.3. Operational excellence

• Monitoring and logging: [E.g., Structured agent logs routed to Cloud Logging]
• Tracing: [E.g., Cloud Trace and trace visualizers for agent reasoning loops and execution paths]
• Continuous evaluation & tooling: [E.g., Agent Evaluation on Gemini Enterprise Agent Platform or ADK evaluation methodologies, MCP Database Toolbox for connection scaling policies]

5.4. Cost optimization

• Data ingestion: [E.g., Low-frequency frame sampling and Base64 JPEG video compression]
• Token optimization: [E.g., Context caching for long system prompts/static lookup databases, structured prompts for concise responses]
• Model selection: [E.g., Starting with smaller models like Gemini Flash and upgrading to Gemini Pro for complex reasoning]

5.5. Performance efficiency

• Media processing & buffering: [E.g., Asynchronous FIFO buffer decoupling incoming audio/video packets from model inference engine]
• Low-latency storage: [E.g., In-memory Memorystore for Redis Cluster for agent schematic vault]
• Compute sizing: [E.g., Fine-tuning Cloud Run memory and CPU limits based on live workloads]

5.6. Sustainability

• Model routing: [E.g., Route simpler tasks to small language models (SLMs) to minimize inference footprint]
• Serverless scaling: [E.g., Cloud Run native autoscaling to scale compute runtimes down to zero during idle periods]

6. Deployment guidance

[Instructions and code for deploying the architecture.]

6.1. Deployment prerequisites

• [Prerequisite 1: E.g., Enabling APIs]
• [Prerequisite 2: E.g., Installing SDK/tools]
• ...and so on

6.2. Step-by-step deployment instructions

1. [Step 1: E.g., Authenticate with Google Cloud]
2. [Step 2: E.g., Initialize Terraform]
3. [Step 3: E.g., Apply Terraform configuration]

7. Validation report

[Instructions and code for verifying that the deployed infrastructure meets the workload requirements.]

7.1. Validation checks

• Deployment dry-run: [E.g., Running terraform plan to preview infrastructure changes]
• Connectivity and routing: [E.g., Verifying VPC egress routing, load balancer service endpoints, and database connection state]
• Security policies: [E.g., Checking IAM role constraints on the agent service accounts and verifying firewall rules]

7.2. Verification scripts

[Lightweight command-line checks or script configurations or cURL/gcloud commands to validate the deployed environment.]

• Execution script 1:

  # E.g., Script to test agent connectivity and endpoint response

• Execution script 2:

  # E.g., Script to verify IAM permissions and service mappings

8. References

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