Play 99
Enterprise AI Governance Hub
Central control plane for all AI models, agents, and APIs across an enterprise.
Central control plane for all AI models, agents, and APIs across an enterprise. Model registry with approval gates, policy enforcement via Azure Policy, cost attribution dashboards, safety monitoring, and regulatory compliance tracking for SOX, EU AI Act, and ISO 42001.
Architecture Pattern
Governance hub: model registration - approval gates - policy enforcement - cost attribution - safety monitoring - compliance reporting
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Governance Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (247 lines), evaluate (104 lines), tune (258 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json - governance analysis and compliance prompts
- config/governance.json - approval thresholds, policy rules, cost models
- config/guardrails.json - compliance coverage minimums, safety monitoring sensitivity
- evaluation/eval.py - Policy compliance 100%, Cost attribution accuracy >98%
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.