Play 90
Network Optimization Agent
5G/LTE capacity planning with digital twin simulation.
5G/LTE network capacity planning with AI-driven anomaly detection, self-healing automation, traffic prediction, and cell tower load balancing using digital twin simulation of network topology. IoT Hub ingests telemetry from cell towers, Stream Analytics detects anomalies in real time, OpenAI generates optimization recommendations, Digital Twins simulates network topology changes, and Cosmos DB stores network state and historical performance data.
Architecture Pattern
Network optimization pipeline: tower telemetry ingestion - anomaly detection - traffic prediction - digital twin simulation - self-healing automation
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Network Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (214 lines), evaluate (105 lines), tune (271 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + IoT Hub inputs + envFile
TuneKit (AI Config)
- config/openai.json - network analysis and optimization prompts
- config/network.json - prediction horizons, anomaly sensitivity, healing triggers
- config/guardrails.json - latency thresholds, coverage minimums
- evaluation/eval.py - Prediction accuracy >90%, Self-healing success >95%
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.