How to monitor and debug your WhatsApp AI agent in production

Deploying and maintaining a reliable WhatsApp AI agent involves tracking key metrics, understanding common failure modes, and implementing debugging strategies to ensure smooth operation. Essential metrics include response time, completion rate, handover rate, message volume, error rate, and cost per conversation. These metrics help identify issues such as delayed responses, structural bugs, or expensive operations. Common problems with AI agents include non-responsiveness due to webhook issues, duplicated replies from lack of deduplication, and incorrect message processing caused by missing scope or staleness checks. Effective logging without compromising user privacy is crucial, focusing on message IDs and timestamps, while avoiding personally identifiable information. Testing in production safely can be achieved through allowlists and gradual rollouts, and the use of tools like CodeWords can automate various safeguards, providing a comprehensive monitoring dashboard and integration with external services for enhanced visibility.

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