Play 43
AI Video Generation
Text-to-video content creation with safety review and cost-controlled batch throughput.
Text-to-video content creation pipeline — prompt-driven video generation with content safety review, batch processing, asset versioning, and cost-controlled throughput for marketing, education, and media teams. Uses Azure Service Bus for async batch queuing and CDN for global asset delivery. Includes watermarking, brand compliance checks, and multi-format export.
Architecture Pattern
Async batch pipeline: prompt → generation → safety review → CDN delivery
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Video Gen Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (243 lines), evaluate (162 lines), tune (269 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + Content Safety key inputs + envFile
TuneKit (AI Config)
- config/openai.json — gpt-4o, temp=0.1
- config/video.json — quality, frame rate, resolution
- config/guardrails.json — content safety, brand compliance
- evaluation/eval.py — Quality >80%, Safety pass rate 100%
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.