How do you implement webhook-based AI integrations for event-driven automation?

Webhook-based AI integration enables real-time responses to business events without polling. Architecture: source systems emit webhooks on events (payment received, ticket created, form submitted), your webhook endpoint receives and validates the event, AI processing occurs (classification, extraction, generation), and results trigger actions in downstream systems. Implementation: create a webhook receiver (Express, FastAPI, or Supabase Edge Function) that validates webhook signatures (HMAC-SHA256), parses the payload, routes to appropriate AI processing, and responds within the source system's timeout (typically 5-30 seconds). For long-running AI tasks: acknowledge the webhook immediately with 200 OK, process asynchronously, and notify via callback URL or update via API. Common patterns: incoming email webhook triggers AI classification and auto-response, payment webhook triggers AI fraud scoring and approval/flag, form submission webhook triggers AI extraction and CRM record creation, and support ticket webhook triggers AI categorization and routing. Security: always verify webhook signatures, use HTTPS endpoints, implement IP allowlisting where possible, validate payload schemas, and use idempotency keys to handle duplicate deliveries. Reliability: implement a retry queue for failed processing, dead letter queue for permanently failed events, and monitoring for webhook delivery delays. Testing: use tools like ngrok for local development, implement webhook replay for debugging, and maintain a comprehensive test suite covering all event types.