Google Cloud solution architecture: Data science workflow with AI agents

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.]

| Component | Recommended Google Cloud product/feature | Justification and

4.2. Architecture diagram

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

%% Example structure
flowchart TD
    User([User]) --> Frontend["Frontend (Cloud Run)"]
    Frontend <--> Coordinator["Root agent (Coordinator Agent)"]

    subgraph Agents["Runtime for agent (Cloud Run)"]
        Coordinator
        Coordinator <--> Analytics["Analytics Agent"]
        Coordinator <--> DBAgents["Database & ML Agents"]
    end

    Agents <--> Model["Gemini Model (Gemini Enterprise Agent Platform)"]
    DBAgents <--> Storage["Database (BigQuery, AlloyDB, BQML)"]

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 and safety: [E.g., Least-privilege agent IAM roles, Model Armor prompt/response sanitization]
Data protection: [E.g., Secret Manager for credentials, Cloud Data Loss Prevention API for sensitive data]
Network security: [E.g., Cloud Run Direct VPC egress, private VPC IPs, regional external Application Load Balancer with Cloud Armor]

5.2. Reliability

Agent and runtime robustness: [E.g., Coordinator agent fallback logic, graceful error handling for SQL/code execution failures]
Scale and rate-limit management: [E.g., Exponential backoff with retries for 429 errors, provisioned model throughput settings]
Execution limits and redundancy: [E.g., Query timeouts, memory/CPU caps on generated code, multi-zone Cloud Run deployment]

5.3. Operational excellence

Monitoring and logging: [E.g., Structured agent logs routed to Cloud Logging, tracing inter-agent communication via Cloud Trace / OpenTelemetry]
Environment isolation: [E.g., Sandboxed python code execution, containerized Cloud Run runtimes]

5.4. Cost optimization

Resource sizing and scaling: [E.g., Cloud Run autoscaling to zero for idle environments, context caching for high input tokens]
Tiered model strategy: [E.g., Routing simple SQL/logical tasks to Gemini Flash and reserving Gemini Pro for complex reasoning]

5.5. Performance efficiency

Database and connectivity performance: [E.g., Cloud Run Direct VPC egress with connection pooling to reduce latency]
Processing and visualization: [E.g., Aggregating data at the database level, returning summarized results or charts instead of raw data]

5.6. Sustainability

Resource utilization: [E.g., Scaling down dev databases during idle hours, serverless Cloud Run adoption]
Carbon-free deployment: [E.g., Selecting Google Cloud regions with high Carbon-Free Energy (CFE) metrics or low CO2 indicators]

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

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

/google-cloud-solution-agentic-ai-data-science-workflow

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