Play 48
AI Model Governance
High✅ Ready
Centralized model lifecycle management with approval gates, A/B testing, and audit trails.
Centralized model lifecycle management — model registry, version control, approval gates, A/B testing, safety evaluation, and audit trails satisfying SOX, EU AI Act, and enterprise risk governance requirements. Uses Azure ML for model registry, Azure Policy for governance enforcement, and Cosmos DB for audit state. Full lineage tracking from training data to production deployment.
Architecture Pattern
Model registry: gated promotion pipeline, automated evaluation, policy-enforced governance
Azure Services
Azure Machine LearningAI FoundryAzure DevOpsCosmos DBAzure MonitorAzure PolicyKey Vault
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Model Gov Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (228 lines), evaluate (141 lines), tune (197 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with ML workspace + OpenAI inputs + envFile
TuneKit (AI Config)
- config/openai.json — evaluation model config
- config/governance.json — approval gates, A/B rules, drift thresholds
- config/guardrails.json — policy enforcement, audit retention
- evaluation/eval.py — Gate accuracy >95%, Drift detection >85%
Tuning Parameters
Approval gate thresholdsDrift detection sensitivityEvaluation frequencyA/B traffic splitAudit retention period
Estimated Cost
Dev/Test
$100–250/mo
Production
$3K–10K/mo