1 min read
Event-Driven AI: Building Reactive Intelligent Systems
I wrote “Event-Driven AI: Building Reactive Intelligent Systems” to share practical, production-minded guidance on this topic.
Event-Driven AI Patterns
Event Sources Processing Outcomes
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Data Changes │────────────►│ AI Inference │─────────────►│ Alerts │
│ User Actions │ │ Enrichment │ │ Actions │
│ System Events│ │ Classification│ │ Updates │
│ Scheduled │ │ Generation │ │ Workflows │
└──────────────┘ └──────────────┘ └──────────────┘
Pattern 1: Event-Triggered Inference
import azure.functions as func
from azure.ai.foundry import AIFoundryClient
from azure.servicebus import ServiceBusClient
import json
app = func.FunctionApp()
ai_client = AIFoundryClient(project="event-ai", credential=DefaultAzureCredential())
@app.service_bus_topic_trigger(
arg_name="message",
topic_name="data-changes",
subscription_name="ai-processor",
connection="ServiceBusConnection"
)
async def process_data_change(message: func.ServiceBusMessage):
"""Process data change events with AI."""
event = json.loads(message.get_body().decode())
if event["type"] == "customer_created":
await enrich_customer(event["data"])
elif event["type"] == "order_placed":
await analyze_order(event["data"])
elif event["type"] == "feedback_received":
await analyze_sentiment(event["data"])
async def enrich_customer(customer: dict) -> dict:
"""Enrich new customer data with AI."""
response = await ai_client.chat.complete_async(
deployment="gpt-4o-mini",
messages=[{
"role": "user",
"content": f"""Analyze this customer data and provide enrichment:
{json.dumps(customer)}
Return JSON with:
- predicted_segment (enterprise/smb/consumer)
- industry_classification
- potential_value (high/medium/low)
- recommended_products: []"""
}]
)
enrichment = json.loads(response.choices[0].message.content)
# Update customer record
await update_customer(customer["id"], enrichment)
# Trigger downstream events
await publish_event("customer_enriched", {
"customer_id": customer["id"],
"enrichment": enrichment
})
return enrichment
Pattern 2: Change Data Capture with AI
from azure.cosmos import CosmosClient
class CDCProcessor:
def __init__(self, cosmos_client: CosmosClient, ai_client: AIFoundryClient):
self.cosmos = cosmos_client
self.ai_client = ai_client
self.container = cosmos_client.get_database_client("main").get_container_client("items")
async def process_change_feed(self):
"""Process Cosmos DB change feed with AI."""
# Start from beginning or continuation token
change_feed = self.container.query_items_change_feed(
is_start_from_beginning=False,
partition_key_range_id="0"
)
for change in change_feed:
await self._process_change(change)
async def _process_change(self, change: dict):
"""Process individual change with AI."""
# Determine change type
if change.get("_deleted"):
change_type = "delete"
elif change.get("_ts") == change.get("_created_ts"):
change_type = "create"
else:
change_type = "update"
# AI-powered change analysis
if change_type == "update":
analysis = await self._analyze_update(change)
if analysis.get("significant_change"):
await self._trigger_workflow(change, analysis)
async def _analyze_update(self, change: dict) -> dict:
"""Analyze if update is significant."""
response = await self.ai_client.chat.complete_async(
deployment="gpt-4o-mini",
messages=[{
"role": "user",
"content": f"""Analyze this data change:
Changed document: {json.dumps(change)}
Determine:
1. Is this a significant change? (affects business metrics, requires action)
2. What type of change? (correction, update, escalation)
3. Should this trigger a workflow?
Return JSON: {{significant_change: bool, change_type: str, trigger_workflow: bool, reason: str}}"""
}]
)
return json.loads(response.choices[0].message.content)
Pattern 3: Event Sourcing with AI
from dataclasses import dataclass
from typing import Any
import uuid
@dataclass
class Event:
id: str
type: str
data: dict
timestamp: datetime
ai_metadata: dict = None
class EventSourcedAI:
def __init__(self, event_store, ai_client):
self.event_store = event_store
self.ai_client = ai_client
async def handle_command(self, command: dict) -> list[Event]:
"""Process command and generate events with AI assistance."""
# Get current state from events
entity_id = command["entity_id"]
current_state = await self._rebuild_state(entity_id)
# AI-assisted decision making
decision = await self._ai_decide(command, current_state)
# Generate events based on decision
events = self._generate_events(command, decision)
# Store events
for event in events:
await self.event_store.append(event)
return events
async def _rebuild_state(self, entity_id: str) -> dict:
"""Rebuild state from event history."""
events = await self.event_store.get_events(entity_id)
state = {"id": entity_id}
for event in events:
state = self._apply_event(state, event)
return state
async def _ai_decide(self, command: dict, current_state: dict) -> dict:
"""Use AI to make decision based on command and state."""
response = await self.ai_client.chat.complete_async(
deployment="gpt-4o",
messages=[{
"role": "system",
"content": """You are a decision engine. Given a command and current state,
determine the appropriate action and any validation errors."""
}, {
"role": "user",
"content": f"""
Command: {json.dumps(command)}
Current State: {json.dumps(current_state)}
Decide:
1. Should this command be accepted?
2. What events should be generated?
3. Any side effects to trigger?
Return JSON: {{
accepted: bool,
rejection_reason: str or null,
events_to_generate: [{{type: str, data: dict}}],
side_effects: [{{type: str, data: dict}}]
}}"""
}]
)
return json.loads(response.choices[0].message.content)
Pattern 4: Saga Orchestration with AI
class AISagaOrchestrator:
"""Orchestrate multi-step workflows with AI decision making."""
def __init__(self, ai_client, service_bus_client):
self.ai_client = ai_client
self.service_bus = service_bus_client
async def execute_saga(self, saga_type: str, initial_data: dict) -> dict:
"""Execute a saga with AI-guided steps."""
saga_id = str(uuid.uuid4())
state = {
"saga_id": saga_id,
"type": saga_type,
"status": "running",
"steps_completed": [],
"data": initial_data
}
while state["status"] == "running":
# AI determines next step
next_step = await self._determine_next_step(state)
if next_step["action"] == "complete":
state["status"] = "completed"
break
if next_step["action"] == "compensate":
await self._compensate(state)
state["status"] = "compensated"
break
# Execute step
try:
result = await self._execute_step(next_step, state)
state["steps_completed"].append({
"step": next_step,
"result": result,
"timestamp": datetime.utcnow().isoformat()
})
state["data"].update(result.get("data_updates", {}))
except Exception as e:
state["last_error"] = str(e)
# AI decides whether to retry, compensate, or fail
recovery = await self._determine_recovery(state, e)
if recovery["action"] == "retry":
continue
elif recovery["action"] == "compensate":
await self._compensate(state)
state["status"] = "compensated"
else:
state["status"] = "failed"
return state
async def _determine_next_step(self, state: dict) -> dict:
"""AI determines the next step in the saga."""
response = await self.ai_client.chat.complete_async(
deployment="gpt-4o",
messages=[{
"role": "user",
"content": f"""Determine the next step for this saga:
Saga Type: {state['type']}
Steps Completed: {json.dumps(state['steps_completed'])}
Current Data: {json.dumps(state['data'])}
Return JSON: {{
action: "execute" | "complete" | "compensate",
step_name: str,
step_config: dict,
reason: str
}}"""
}]
)
return json.loads(response.choices[0].message.content)
Pattern 5: Event-Driven Notifications
class AINotificationEngine:
"""Generate intelligent notifications from events."""
def __init__(self, ai_client, notification_service):
self.ai_client = ai_client
self.notification_service = notification_service
async def process_event(self, event: dict):
"""Process event and generate appropriate notifications."""
# Determine notification need
notification_spec = await self._determine_notification(event)
if not notification_spec["should_notify"]:
return
# Generate personalized messages for each recipient
for recipient in notification_spec["recipients"]:
message = await self._generate_message(event, recipient)
await self.notification_service.send(
recipient=recipient,
channel=notification_spec["channel"],
message=message
)
async def _determine_notification(self, event: dict) -> dict:
"""Determine if and how to notify."""
response = await self.ai_client.chat.complete_async(
deployment="gpt-4o-mini",
messages=[{
"role": "user",
"content": f"""Analyze this event for notification:
Event: {json.dumps(event)}
Determine:
1. Should this generate a notification?
2. Who should be notified?
3. What channel (email/slack/sms/push)?
4. What urgency level?
Return JSON: {{
should_notify: bool,
recipients: [{{id: str, role: str}}],
channel: str,
urgency: "immediate" | "normal" | "digest",
reason: str
}}"""
}]
)
return json.loads(response.choices[0].message.content)
async def _generate_message(self, event: dict, recipient: dict) -> str:
"""Generate personalized notification message."""
response = await self.ai_client.chat.complete_async(
deployment="gpt-4o-mini",
messages=[{
"role": "user",
"content": f"""Generate a notification message:
Event: {json.dumps(event)}
Recipient Role: {recipient['role']}
Write a clear, concise message appropriate for this recipient.
Be specific and actionable."""
}],
max_tokens=150
)
return response.choices[0].message.content
Best Practices
- Idempotency: Ensure AI processing is idempotent for retry safety
- Async processing: Use queues to decouple event production from AI processing
- Dead letter queues: Handle failed AI processing gracefully
- Cost awareness: Monitor AI costs per event type
- Observability: Trace events through AI processing
Event-driven AI enables responsive, intelligent systems. Start with simple event handlers and evolve to complex orchestration as needed.\n\n## Takeaways\n\nAdd a concise, personal takeaway and recommended next steps here.\n