Multi-agent AI creation infrastructure

Building infrastructure for AI-generated video, apps, and creative workflows.

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

Q-Star AI already operates multiple AI generation workflows across video, app generation, agent orchestration, and automated media production. We are preparing to consolidate these workloads on Google Cloud.

Google Cloud Use Case View Evidence
AI Video AI Agents App Generation Workflow Automation Multi-Model Infrastructure
prompt "Generate a vertical drama trailer and companion app"
agent.plan story, assets, scenes, UI, render tasks
model.route Gemini | OpenAI | Claude | local inference
queue.dispatch video, image, audio, code generation
cloud.render assemble, encode, package, deploy
output video + APK + workflow audit trail
5 infrastructure modules unified under one platform
6x projected monthly workload growth over 6 months
24/7 async agent, rendering, and routing workloads

Google Cloud Startup Credits use case.

We are applying for Google Cloud credits to migrate active AI generation workloads from fragmented infrastructure into a scalable cloud-native platform.

Current status

Q-Star AI already operates multiple AI generation workflows across video, app generation, agent orchestration, and automated media production. We are preparing to consolidate these workloads on Google Cloud for reliability, monitoring, and capacity expansion.

  • Migration of agent workloads to Cloud Run and GKE.
  • Gemini API usage for text, code, planning, and multimodal generation.
  • Cloud Storage for generated videos, images, APKs, and prompt archives.
  • Pub/Sub for async task dispatch, rendering queues, retries, and workload fan-out.
  • Cloud Logging and Cloud Monitoring for reliability, observability, and cost control.
  • GPU workloads for rendering, image and video processing, and batch generation.

One workflow from prompt to generated asset.

The infrastructure turns a user request into a planned, routed, generated, rendered, and stored output. This creates sustained demand for AI APIs, GPU workloads, object storage, queues, logs, and cloud rendering.

1

Prompt

User requests a video, drama concept, app, or automated workflow.

2

Agent Planning

AgentOS decomposes the request into scripts, assets, tasks, and checks.

3

Model Routing

OpenClaw Stack selects models for text, code, image, audio, and video.

4

Asset Generation

Specialized workers generate scenes, voice, music, UI, and code.

5

Video / App Output

Outputs are assembled, encoded, packaged, and prepared for delivery.

6

Cloud Rendering

Async compute handles rendering, storage, observability, and retries.

Unified infrastructure modules.

Q-Star AI packages creation capabilities into a single infrastructure layer with shared agent orchestration, model routing, storage, and cloud execution.

QClip

AI Short Video Generation

Turns scripts, prompts, and assets into short-form videos with automated editing and rendering.

Drama Engine

AI Storytelling Pipeline

Generates short-drama concepts, scenes, dialogue, characters, and production-ready outputs.

APK Generator

AI App Builder

Creates mobile application prototypes and packaged apps from natural-language requirements.

AgentOS

Multi-Agent Workflow System

Coordinates planning, execution, verification, retries, and human review across creation tasks.

OpenClaw Stack

AI Orchestration Infrastructure

Routes work across models, tools, queues, compute services, storage, and monitoring layers.

Cloud architecture designed for scaling AI workloads.

The system is built around async task execution, multi-model routing, heavy media processing, and persistent storage. Google Cloud credits would accelerate migration, scaling, and production hardening.

FrontendWeb apps, API clients, admin dashboards, creator interfaces
GatewayNginx, API gateway, auth, rate limits, request tracing
Agent LayerAgentOS, Hermes, task planning, execution, review loops
AI LayerGemini, OpenAI, Claude, local inference, model routing
ProcessingVideo rendering, image generation, audio synthesis, APK builds
StorageObject storage, metadata database, prompt and output archives
QueueAsync task queue, retries, scheduled workloads, priority lanes
MonitoringLogs, health checks, cost tracking, usage analytics
Q-Star AI cloud architecture Q-Star AI Cloud Architecture Frontend / API Gateway Auth / Limits Agent Layer AgentOS / Hermes AI Model Layer Gemini / OpenAI / Claude Task Queue Async workloads Processing Video / Image / Audio Storage Objects / Metadata Monitoring Cost Analytics

Evidence for the application.

The application package should combine public demos, deployed systems, current workload proof, and generated outputs so Google can verify this is an operating AI platform, not only a concept website.

Live demo links

Public pages and hosted demos that show Q-Star AI workflows, outputs, and platform positioning.

Open evidence page

Existing deployed systems

Current domains, servers, APIs, workflow services, and product modules already deployed or running internally.

View architecture

Server and log screenshots

Backend logs, queue activity, render workers, storage metrics, model calls, and monitoring screenshots.

Evidence checklist

Example generated outputs

Generated videos, images, APK artifacts, app prototypes, workflow traces, and creative production examples.

Output checklist

Current API and workload evidence

Daily generation counts, token usage, GPU hours, async jobs, storage growth, and API request volume.

View projection

Application materials

Copy-ready Google Cloud Startup Credits answers, one-page pitch, architecture notes, and demo video script.

Open application answers

Projected cloud usage.

Based on current internal workloads, active demos, and planned early-access rollout, these projections model the infrastructure required to scale from today's internal usage to production workloads. Final application materials should attach live logs, current server data, screenshots, and demo evidence.

Workload Current baseline 6-month target Cloud impact
Daily generations 300 20,000 Model APIs, queue throughput, observability
Video rendering minutes 1,200 / month 80,000 / month Compute, GPU workers, storage egress
Token consumption 60M / month 2B / month Gemini APIs, routing, prompt logs
GPU inference hours 120 / month 3,000 / month Media generation, batch jobs, rendering
Storage growth 0.5 TB 12 TB Object storage, generated assets, archives

Built to scale on Google Cloud.

Q-Star AI is preparing to consolidate compute, storage, queueing, model access, and monitoring on Google Cloud to support high-volume AI creation workloads.

Technical Notes