How do I handle tool errors in AI calling?
Detect and Classify Errors
When a tool call fails, the API or function typically returns an error object or an HTTP status code. Your AI model should be instructed to check for errors in the tool's output and handle them accordingly. Common error types include network timeouts, invalid input, rate limits, and internal server errors.
Classify errors as transient (e.g., timeouts, rate limits) or permanent (e.g., invalid parameters, missing permissions). Transient errors may succeed on retry, while permanent errors require a different approach.
- Timeout: retry with exponential backoff
- Rate limit: wait and retry after delay
- Invalid input: ask user for correction
- Permission denied: escalate or use fallback tool
- Server error: retry once, then fallback
Implement Retry and Fallback Strategies
For transient errors, implement a retry mechanism with exponential backoff and a maximum number of attempts. For example, retry up to 3 times with delays of 1s, 2s, and 4s. If the tool still fails, fall back to an alternative method or inform the user.
Fallbacks can include using a different tool, returning cached data, or providing a helpful message like 'I couldn't retrieve that information right now. Please try again later.' The AI should be trained to recognize when to use each fallback.
Log and Learn from Errors
Log all tool errors with context: the input parameters, error message, timestamp, and user ID. This helps you identify patterns and improve tool reliability. You can also use error logs to fine-tune your AI model to handle specific errors better.
Consider adding error handling instructions to your system prompt. For example: 'If a tool returns an error, apologize and suggest an alternative action.' This ensures consistent behavior across different tools.
Common mistakes
- Assuming all errors are the same and retrying blindly without checking the error type.
- Not informing the user when a tool fails, leaving them confused.
- Ignoring error logs and missing opportunities to fix recurring issues.