Play 86
Public Safety Analytics
Crime pattern prediction with resource allocation optimization.
Crime pattern prediction with resource allocation optimization, community sentiment analysis from social media and 311 calls, and real-time incident dashboard for law enforcement and emergency management. OpenAI powers pattern analysis and report generation, Machine Learning builds predictive models, Event Hubs ingests real-time incident streams, Cosmos DB stores historical data, and Stream Analytics processes live feeds for dashboard updates.
Architecture Pattern
Public safety pipeline: incident streams - pattern detection - resource optimization - sentiment analysis - real-time dashboard
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Safety Analytics Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (196 lines), evaluate (106 lines), tune (234 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + Maps inputs + envFile
TuneKit (AI Config)
- config/openai.json - pattern analysis and report generation prompts
- config/safety.json - prediction windows, zone definitions, alert levels
- config/guardrails.json - privacy filtering, bias mitigation, fairness thresholds
- evaluation/eval.py - Prediction accuracy >80%, Privacy compliance 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.