Best Practices for Production

Essential best practices for deploying and maintaining AI agents in production environments.

Performance Optimization

• Cache aggressively: Cache API responses where appropriate to reduce latency
• Use CDN: Leverage content delivery networks for static assets
• Batch requests: Combine multiple operations into single API calls
• Compress data: Enable gzip compression for large payloads
• Async processing: Use webhooks for long-running operations

Error Handling

• Implement comprehensive error logging
• Use exponential backoff for retries
• Set appropriate timeout values
• Handle rate limits gracefully
• Monitor error rates and set up alerts

Security

• Never expose API keys in client-side code
• Use environment variables for secrets
• Implement IP whitelisting when possible
• Rotate API keys regularly
• Enable two-factor authentication
• Review access logs periodically

Monitoring & Observability

• Set up uptime monitoring
• Track key performance metrics (latency, error rate, throughput)
• Configure alerting for anomalies
• Use distributed tracing for complex workflows
• Regularly review analytics dashboard

Scalability

• Design for horizontal scaling
• Use load balancing across multiple instances
• Implement connection pooling
• Monitor resource utilization
• Plan capacity based on growth projections

Testing Strategy

• Maintain comprehensive test coverage
• Use staging environment that mirrors production
• Perform load testing before major releases
• Implement canary deployments
• Have rollback procedures ready