How to give your Cursor AI Agent a Webhook Listener
Cursor agents are powerful but reactive. Learn how to give your AI agent a 'Mailbox' so it can autonomously react to external events like payments or GitHub alerts.
The Cursor Tool Primitive
# Add this to your project so Cursor can 'see' it
def check_mailbox():
"""Check for new external triggers from FetchHook."""
import requests
res = requests.get("https://api.fetchhook.app/api/v1/stash_123",
headers={"Authorization": "Bearer fh_xxx"})
return res.json()#Why do Cursor agents need a mailbox?
Normally, you have to tell Cursor what to do through chat. But what if you want your agent to work while you're away? What if you want it to react to external events (Stripe payments, GitHub PRs, Shopify orders) without you initiating the conversation? By giving Cursor a tool to 'check the mail,' the agent can autonomously poll for work. You point your services (Stripe, GitHub, etc.) to FetchHook, and the agent periodically checks for new events to action. This transforms Cursor from an on-demand assistant into an autonomous automation agent.
#How does the mailbox pattern work with Cursor?
Cursor has access to your codebase and can execute functions. By adding a check_mailbox() function to your project, you give Cursor a programmatic way to discover external events. In your .cursorrules or system prompt, instruct the agent: 'Every 60 seconds, call check_mailbox(). If new events exist, process them according to their type.' Cursor will autonomously run this loop, pulling events from FetchHook and executing the appropriate handlers (process payment, merge PR, send notification, etc.).
The Tool Implementation (Python)
# tools/fetchhook.py
import requests
import os
FETCHHOOK_API_KEY = os.getenv("FETCHHOOK_API_KEY")
SOURCE_ID = "stash_cursor_agent"
def check_mailbox():
"""
Check for new external events from FetchHook.
Cursor agent calls this periodically to discover work.
Returns list of events to process.
"""
response = requests.get(
f"https://api.fetchhook.app/api/v1/{SOURCE_ID}",
headers={"Authorization": f"Bearer {FETCHHOOK_API_KEY}"}
)
if response.status_code != 200:
print(f"Failed to fetch events: {response.status_code}")
return []
events = response.json().get('events', [])
print(f"📬 Mailbox check: {len(events)} new events")
return events
def process_event(event):
"""Process a single event based on type."""
event_type = event['payload'].get('type')
if 'stripe' in event.get('provider', '').lower():
if event_type == 'payment_intent.succeeded':
# Cursor agent processes payment
fulfill_order(event['payload'])
elif 'github' in event.get('provider', '').lower():
if event_type == 'pull_request.opened':
# Cursor agent reviews PR
review_pull_request(event['payload'])
else:
print(f"Unknown event type: {event_type}")#How do I configure .cursorrules for autonomous operation?
The .cursorrules file tells Cursor how to behave. To create an autonomous agent, add instructions for continuous operation: Check mailbox every N seconds, Process events by type, Log all actions for auditing, Handle errors gracefully (don't crash the loop). This configuration transforms Cursor from a reactive chatbot into a proactive automation agent that works 24/7 even when you're not at your computer.
.cursorrules Configuration
# .cursorrules - Autonomous Webhook Agent
You are an autonomous agent that monitors external events and takes action.
## Core Loop
1. Every 60 seconds, call `tools.fetchhook.check_mailbox()`
2. For each event returned:
- Verify `signature_verified` is true
- Process based on event type (see handlers below)
- Log all actions to `agent.log`
3. If errors occur, log and continue (don't crash loop)
## Event Handlers
- Stripe `payment_intent.succeeded`: Call `fulfill_order()`
- GitHub `pull_request.opened`: Call `review_pull_request()`
- Shopify `orders/create`: Call `process_order()`
## Error Handling
- Network errors: Retry after 30 seconds
- Processing errors: Skip event and log
- Never stop the loop unless explicitly told
## Logging
Always log: event type, processing result, timestamp#What is the 'Continuous Agent' pattern?
The Continuous Agent pattern turns Cursor into a long-running automation daemon. Instead of you typing '@cursor do X', the agent runs autonomously in the background: It polls FetchHook for events, Processes them according to predefined logic, Logs all actions for your review, Only interrupts you when human approval is needed (via Human-in-the-Loop pattern). This is perfect for: Processing payments while you sleep, Auto-triaging GitHub issues, Sending order confirmations automatically, Running scheduled data syncs. You set it up once, and it runs continuously.
Continuous Agent Runner (main.py)
# main.py - Start the autonomous agent
import time
import logging
from tools.fetchhook import check_mailbox, process_event
logging.basicConfig(
filename='agent.log',
level=logging.INFO,
format='%(asctime)s - %(message)s'
)
def continuous_agent():
"""
Autonomous agent loop.
Cursor executes this continuously.
"""
logging.info("🤖 Cursor agent started")
while True:
try:
# Check mailbox
events = check_mailbox()
# Process all events
for event in events:
if event.get('signature_verified'):
logging.info(f"Processing: {event['id']}")
process_event(event)
else:
logging.warning(f"Skipping unverified: {event['id']}")
# Wait before next check
time.sleep(60)
except KeyboardInterrupt:
logging.info("Agent stopped by user")
break
except Exception as e:
logging.error(f"Agent error: {e}")
time.sleep(30) # Back off on errors
if __name__ == "__main__":
continuous_agent()#What are real-world use cases for Cursor + FetchHook?
Autonomous Cursor agents excel at: (1) Payment processing: Stripe webhooks trigger order fulfillment while you sleep, (2) Code review automation: GitHub PR webhooks trigger Cursor to review code quality, suggest improvements, (3) Customer support: Intercom webhooks trigger AI-powered draft responses for tickets, (4) Data pipeline orchestration: Webhook events trigger data transformations and ML model updates, (5) Compliance automation: Clerk user signup webhooks trigger background checks and KYC processes. The pattern: external event → FetchHook → Cursor processes autonomously.
#How do I debug and monitor the autonomous agent?
Debugging autonomous agents requires good logging and observability: (1) Log every event processed with timestamp and result, (2) Use structured logging (JSON) for easy parsing, (3) Set up log aggregation (Datadog, CloudWatch, Loki) for monitoring, (4) Add health checks: agent pings a heartbeat endpoint every minute, (5) Implement alerting: if no heartbeat for 5 minutes, send alert. Review logs daily to catch edge cases and improve event handlers.
Structured Logging for Monitoring
import json
import logging
# JSON structured logging for easy monitoring
class JsonFormatter(logging.Formatter):
def format(self, record):
log_obj = {
"timestamp": self.formatTime(record),
"level": record.levelname,
"message": record.getMessage(),
"agent": "cursor-webhook-agent"
}
return json.dumps(log_obj)
handler = logging.StreamHandler()
handler.setFormatter(JsonFormatter())
logger = logging.getLogger()
logger.addHandler(handler)
# Now logs are JSON for easy monitoring
logger.info("Processed payment event",
extra={"event_id": "evt_123", "amount": 4999})#Does this work with Windsurf and other AI editors?
Yes! The pattern is the same for any AI editor that can execute code: Windsurf, Cody, GitHub Copilot Workspace, Continue.dev. Add the check_mailbox() tool to your codebase, configure the agent's system prompt to run a continuous loop, and let it poll FetchHook for events. The key is that the AI has filesystem access and can run Python/Node scripts autonomously. FetchHook is editor-agnostic—it just provides a mailbox API that any agent can pull from.
Agent Protocol Instruction