Enhance Error Handling

[mislavjc]

Description

The current error handling system lacks comprehensive coverage and user-friendly error messages. When errors occur during AI operations, browser automation, or API calls, users often receive generic error messages or, in some cases, no feedback at all. This leads to poor user experience and makes debugging issues more difficult.

Proposed Solution

1. Standardize Error Response Format:

   • Create a consistent error response structure
   • Include error codes, messages, and suggested actions
   • Add support for different severity levels
   • Implement stack trace capture for debugging (in development mode)

2. Enhanced Error Categories:

   • AI Model Errors (token limits, model unavailability)
   • Browser Automation Errors (element not found, navigation failures)
   • API Integration Errors (rate limits, authentication issues)
   • User Input Validation Errors
   • System/Infrastructure Errors

3. User-Facing Error Handling:

   • Implement friendly error messages for common scenarios
   • Add retry mechanisms where appropriate
   • Provide guidance on how to resolve common issues
   • Show different levels of detail based on user role/mode

4. Error Logging and Monitoring:

   • Implement comprehensive error logging
   • Add error tracking and analytics
   • Create error reporting dashboard
   • Set up alerts for critical errors

Alternatives Considered

1. Client-Side Only Error Handling:

   • Handle all errors in the frontend
   • Pros: Simpler implementation
   • Cons: Less robust, missing server context

2. Third-Party Error Tracking:

   • Use services like Sentry or Rollbar
   • Pros: Ready-made solution
   • Cons: Additional cost, external dependency