# Q-Star AI Cloud Architecture

## Positioning

Q-Star AI is building a multi-agent AI creation infrastructure for video, apps, and automated creative workflows.

## Architecture overview

The platform uses a cloud-native architecture to convert prompts into AI-generated outputs. Each request moves through API intake, agent planning, multi-model routing, async task execution, media processing, storage, analytics, and monitoring.

## Google Cloud services

| Layer | Google Cloud service | Role in Q-Star AI |
| --- | --- | --- |
| AI generation | Vertex AI, Gemini API | Agent planning, prompt processing, creative generation, model evaluation |
| Backend services | Cloud Run | API services, agent workers, model-routing services, webhook handlers |
| Object storage | Cloud Storage | Generated videos, images, audio, APKs, prompt archives, workflow artifacts |
| Async queue | Pub/Sub | Task fan-out, rendering queues, retries, scheduled jobs, workload coordination |
| Analytics | BigQuery | Usage analytics, cost reporting, workload forecasting, generation telemetry |
| Observability | Cloud Monitoring, Cloud Logging | Health checks, worker metrics, failure analysis, latency and cost monitoring |
| Compute | GPU-enabled Compute Engine or GKE | Media generation, rendering, encoding, inference experiments |
| Security and access | IAM, Secret Manager, Cloud Armor | Service permissions, API keys, secrets, endpoint protection |

## Request flow

1. A user submits a prompt through the web interface or API.
2. Cloud Run receives the request and passes it to the AgentOS planning service.
3. AgentOS decomposes the prompt into tasks such as story, script, image, audio, video, app UI, code, render, and review.
4. The model router selects the best model or provider for each task, including Vertex AI and Gemini API where applicable.
5. Pub/Sub distributes async work to generation, rendering, packaging, and verification workers.
6. Processing workers generate and transform assets.
7. Cloud Storage stores generated assets, app build artifacts, intermediate outputs, and final deliverables.
8. BigQuery receives usage events, workload metrics, generation telemetry, and cost data.
9. Cloud Monitoring and Cloud Logging track errors, latency, worker health, throughput, and service reliability.

## Mermaid architecture diagram

```mermaid
flowchart TD
  A["Web / API Clients"] --> B["Cloud Run API Gateway"]
  B --> C["AgentOS Planning Service"]
  C --> D["Model Router"]
  D --> E["Vertex AI / Gemini API"]
  D --> F["External Model APIs"]
  C --> G["Pub/Sub Task Queue"]
  G --> H["Cloud Run Agent Workers"]
  G --> I["GPU Processing Workers"]
  H --> J["Video / Image / Audio / App Generation"]
  I --> J
  J --> K["Cloud Storage"]
  J --> L["BigQuery Usage Events"]
  B --> M["Cloud Logging / Monitoring"]
  C --> M
  H --> M
  I --> M
  K --> N["Delivery API / Output Dashboard"]
```

## Why this architecture fits Google Cloud

Q-Star AI has cloud workloads that grow with usage:

- More prompts increase Vertex AI and Gemini API consumption.
- More workflows increase Cloud Run service execution.
- More media generation increases GPU and rendering workloads.
- More async jobs increase Pub/Sub throughput.
- More generated outputs increase Cloud Storage usage.
- More users require BigQuery analytics and Monitoring.

This makes Google Cloud a strong infrastructure fit because the platform needs scalable compute, managed AI services, durable storage, queueing, analytics, and observability in one ecosystem.

## Initial migration plan

| Phase | Scope |
| --- | --- |
| Phase 1 | Deploy public landing page, API gateway, and core backend services on Cloud Run |
| Phase 2 | Move generated media and app artifacts into Cloud Storage |
| Phase 3 | Introduce Pub/Sub for async generation, render, and retry workflows |
| Phase 4 | Integrate Vertex AI and Gemini API into model routing and agent planning |
| Phase 5 | Add BigQuery usage analytics and Cloud Monitoring dashboards |
| Phase 6 | Scale GPU workers for media generation and rendering workloads |