Best Practices & Limitations¶

⚠️ Common Mistakes¶

Metaphor:
Pitfalls to Avoid – Like walking through a rocky path, here are typical issues that can trip you up.

Checklist:

• ✅ Missing Configuration: Always double-check that your YAML config includes all required keys (e.g., server_url, webhook_path).
• ✅ Port Conflicts: Ensure the port your Client listens on is not already in use by another service.
• ✅ Unreachable Webhook: Confirm that your webhook endpoint is exposed and accessible from the outside (use tools like ngrok for local testing).
• ✅ No Authentication: Never leave webhooks unsecured; always implement signature validation.
• ✅ Silent Failures: Set up proper logging and error handling; don’t assume everything works if no error is shown.

🚫 Limitations¶

While the MFO Client is designed to be robust, it's important to understand its operational boundaries:

• Stateless Design: The Client primarily acts as an event forwarder and taskflow executor. It generally does not store data persistently between requests or across restarts (e.g., webhook payloads or task states, unless explicitly designed into a taskflow that uses external storage). Its core role is to process and forward events or execute defined workflows, relying on the MFO Server or other systems for persistent state management.

• External Systems Dependency: The Client's functionality is inherently tied to external systems: the sources of webhooks/events and the MFO Server it communicates with. The availability and performance of these external systems directly impact the Client's operations. Ensure these dependencies are monitored.

• Error Handling and Resilience Configuration: The MFO Client's API communication layer (via BaseClient) can be configured with sophisticated error handling mechanisms, including Retry Policies (with strategies like exponential backoff) and Circuit Breakers. This allows for resilience against transient network issues or temporary MFO server unavailability. Refer to Section 2.7 (Error Handling and Retry Logic) and Section 6.1 (Error Handling) of the main MFO Orchestrator documentation for detailed explanations and configuration options. However, while the client can retry operations, advanced patterns like persistent Dead-Letter Queues (DLQs) for events that fail repeatedly (even after retries) are typically an integration concern. If such guarantees are needed, the client's error handling should be integrated with an external queuing system or a persistent store managed by your application logic or a dedicated task in a workflow.

• Local Taskflow Engine Scope: The built-in taskflow engine executes workflows locally within the client instance. It's designed for orchestrating client-side logic and interactions with the MFO server. It is not a distributed workflow engine; for highly complex, distributed, or long-running stateful orchestrations beyond the client's scope, consider leveraging the MFO server's own workflow capabilities or dedicated orchestration platforms.

📈 Scaling Advice¶

When to Split into Multiple Clients¶

Consider deploying multiple Clients when:

• 🔄 Traffic Volume: You expect high throughput and need to balance the load.
• 🌍 Geographical Needs: Clients should be closer to external systems (reduces latency).
• 🏢 Organizational Separation: Different teams or departments require isolated instances for security or operational reasons.
• 🔧 Service Specialization: One Client handles CRM events; another handles IoT device inputs, etc.

🖥️ Monitoring Best Practices¶

• Real-Time Logs:
  Pipe logs into a monitoring system like ELK Stack, Datadog, or CloudWatch.

• Health Checks:
  Add /health endpoints to easily verify uptime.

• Metrics Tracking:
  Track event count, success/failure ratios, and latency using Prometheus + Grafana.

• Alerting:
  Set up alerts for:

• Unusual error spikes
• Webhook failures
• Service downtime

By following these best practices and understanding the limitations, you can ensure your Client MindFlight AI deployment remains reliable, scalable, and secure.