Play 63
Fraud Detection Agent
Real-time transaction fraud detection with sub-100ms latency and explainable decisions.
Agentic fraud detection system that analyzes transactions in real time using behavioral anomaly scoring, velocity checks, device fingerprinting, and network graph analysis. Azure Event Hubs ingests transaction streams, Stream Analytics applies windowed pattern detection, and GPT generates human-readable explanations for every flagged decision — meeting regulatory compliance requirements. Achieves sub-100ms p99 latency with full audit trails in Cosmos DB.
Architecture Pattern
Real-time streaming: multi-signal scoring, deterministic rules, explainable decisions
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Fraud Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (211 lines), evaluate (121 lines), tune (182 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 — gpt-4o-mini for real-time, gpt-4o for investigation
- config/guardrails.json — latency budget, explainability rules
- evaluation/eval.py — Precision >95%, Latency p99 <100ms
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.