Play 45 — Real-Time Event AI

Real-time event-driven AI processing — streaming ingestion via Event Hubs, rule-based + LLM batch enrichment, adaptive anomaly detection with rolling Z-score, pattern matching, deduplication, checkpointing, dead letter queues, and multi-channel alerting.

Architecture

Component	Azure Service	Purpose
Event Ingestion	Azure Event Hubs (8 partitions)	High-throughput event streaming
Event Consumers	Azure Container Apps (2-8 replicas)	Parallel partition processing
AI Enrichment	Azure OpenAI (GPT-4o-mini)	Batch classification for unknown events
Anomaly Detection	Custom (rolling Z-score)	Adaptive threshold anomaly scoring
Checkpointing	Azure Blob Storage	Consumer position tracking
Dead Letter Queue	Event Hubs (DLQ)	Failed event retention + retry
Alerting	Teams/Email/PagerDuty	Multi-channel anomaly notifications
Secrets	Azure Key Vault	Connection strings, API keys

📐 Full architecture details

Aspect	Play 20 (AIOps)	Play 45 (Event AI)	Play 37 (AI DevOps)
Input	Infrastructure metrics	Any event stream (IoT, clickstream, txn)	CI/CD + incidents
Processing	Batch analytics	Real-time streaming (<100ms P50)	Incident-triggered
AI Method	Log analysis	Rule→LLM hybrid, batch enrichment	Incident triage
Detection	Log anomalies	Adaptive Z-score anomaly detection	Deployment risk
Scale	Thousands of metrics	Millions of events/day	Incidents/day
Output	Dashboard + alerts	Enriched events + anomaly alerts	Runbook execution

DevKit Structure


45-realtime-event-ai/
├── agent.md                              # Root orchestrator with handoffs
├── .github/
│   ├── copilot-instructions.md           # Domain knowledge (<150 lines)
│   ├── agents/
│   │   ├── builder.agent.md              # Pipeline + anomaly + patterns
│   │   ├── reviewer.agent.md             # Throughput + checkpoints + dedup
│   │   └── tuner.agent.md                # Batch size + thresholds + cost
│   ├── prompts/
│   │   ├── deploy.prompt.md              # Deploy pipeline
│   │   ├── test.prompt.md                # Simulate event streams
│   │   ├── review.prompt.md              # Audit processing
│   │   └── evaluate.prompt.md            # Measure throughput + accuracy
│   ├── skills/
│   │   ├── deploy-realtime-event-ai/     # Full deploy with Event Hubs + consumers
│   │   ├── evaluate-realtime-event-ai/   # Throughput, anomaly, enrichment, alerts
│   │   └── tune-realtime-event-ai/       # Batch, thresholds, LLM ratio, cost
│   └── instructions/
│       └── realtime-event-ai-patterns.instructions.md
├── config/                               # TuneKit
│   ├── openai.json                       # LLM enrichment model + batch config
│   ├── guardrails.json                   # Anomaly thresholds, detection params
│   └── agents.json                       # Consumer scaling, checkpoint, DLQ
├── infra/                                # Bicep IaC
│   ├── main.bicep
│   └── parameters.json
└── spec/                                 # SpecKit
    └── fai-manifest.json

Quick Start


# 1. Deploy event pipeline
/deploy
 
# 2. Simulate event streams
/test
 
# 3. Audit processing reliability
/review
 
# 4. Measure throughput and anomaly detection
/evaluate

Key Metrics

Metric	Target	Description
Throughput	> 1000 evt/s	Sustained event processing rate
P95 Latency	< 500ms	Event-to-enrichment time
Anomaly F1	> 85%	Detection precision + recall
Enrichment Accuracy	> 92%	Combined rule + LLM classification
LLM Usage Rate	< 10%	Events needing LLM (rest rule-based)
Alert Precision	> 85%	Actionable alerts vs total

Estimated Cost

Service	Dev/mo	Prod/mo	Enterprise/mo
Azure Event Hubs	$12	$90	$600
Azure Functions	$0	$120	$350
Azure OpenAI	$30	$250	$900
Azure Cosmos DB	$5	$140	$700
Azure SignalR Service	$0	$50	$250
Azure Stream Analytics	$25	$150	$600
Key Vault	$1	$5	$15
Application Insights	$0	$30	$100
Total	$73	$835	$3,515

Estimates based on Azure retail pricing. Actual costs vary by region, usage, and enterprise agreements.

💰 Full cost breakdown

WAF Alignment

Pillar	Implementation
Reliability	Checkpointing, deduplication, DLQ, at-least-once delivery
Performance Efficiency	8-partition parallel processing, batch enrichment, auto-scaling
Cost Optimization	Rule-first/LLM-fallback, gpt-4o-mini, LLM result caching
Operational Excellence	Telemetry, alert aggregation, consumer lag monitoring
Security	Key Vault for connection strings, managed identity
Responsible AI	Adaptive thresholds reduce alert fatigue, transparent scoring

FAI Manifest

Field	Value
Play	`45-realtime-event-ai`
Version	`1.0.0`
Knowledge	T3-Production-Patterns, O1-Semantic-Kernel, F1-GenAI-Foundations, R3-Deterministic-AI
WAF Pillars	performance-efficiency, reliability, cost-optimization, operational-excellence
Groundedness	≥ 85%
Safety	0 violations max