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Effective error handling is essential for creating reliable, user-friendly tool-using agents. By properly managing error cases, you can ensure that agents degrade gracefully when issues occur, provide helpful feedback to users, and maintain trust in automated systems.

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The Importance of Error Handling

Robust error handling delivers multiple benefits:

Reliability

Agents continue functioning even when components fail

User Experience

Informative messages rather than confusing failures

Trust

Consistent behavior builds confidence in AI systems

Maintainability

Easier diagnostics and troubleshooting

Resilience

Recovery from temporary issues without intervention

Visibility

Clear insights into system performance and issues

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Error Categories in Tool-Using Agents

Tool-using agents can encounter several categories of errors:
  • Input Errors
  • Execution Errors
  • Data Errors
  • Agent Errors
Issues with the parameters or inputs provided to tools.Common examples:
  • Missing required parameters
  • Invalid parameter formats
  • Parameter validation failures
  • Value constraint violations
  • Inconsistent parameter combinations
Typical causes:
  • Misinterpretation of user requests
  • Incomplete information from users
  • LLM extraction errors
  • Schema misalignment
  • User input errors

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Error Handling Strategy

A comprehensive error handling strategy includes several key components:
1

Error Prevention

Implement measures to prevent errors before they occur.Key techniques:
  • Thorough parameter validation
  • Pre-execution checks and confirmations
  • Clear tool selection criteria
  • Preventive maintenance
  • Proactive monitoring
2

Error Detection

Identify errors quickly and accurately when they happen.Key techniques:
  • Comprehensive error checking
  • Explicit error codes and types
  • Health checks and heartbeats
  • Anomaly detection
  • Timeout monitoring
3

Error Recovery

Implement mechanisms to recover from errors when possible.Key techniques:
  • Automatic retries with backoff
  • Fallback mechanisms
  • Circuit breakers
  • Alternative tool selection
  • Partial result handling
4

Error Communication

Provide clear, actionable information about errors.Key techniques:
  • User-friendly error messages
  • Context-appropriate detail level
  • Actionable suggestions
  • Consistent error formats
  • Progress communication
5

Error Logging and Analysis

Capture error data for improvement and monitoring.Key techniques:
  • Structured error logging
  • Correlation identifiers
  • Context preservation
  • Error aggregation and analysis
  • Trend monitoring

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Implementing Error Handling in Prisme.ai

Prisme.ai provides several mechanisms for implementing robust error handling:
  • AI Knowledge
  • AI Builder
Configure error handling in no-code tool integrations.Key capabilities:
  • Built-in error handling for standard tools
  • Error response configuration
  • LLM guidance for error scenarios
  • User communication templates
  • Error recovery strategies

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Error Handling Patterns

[… the previously provided patterns continue here unchanged …]
Provide alternative methods when primary approaches fail.Implementation example:
slug: weather-forecast-tool
do:
  # Try primary weather service
  - try:
      do:
        - PrimaryWeatherAPI.getForecast:
            location: '{{event.data.parameters.location}}'
            days: '{{event.data.parameters.days || 3}}'
            output: forecastData

        # Return forecast data
        - set:
            name: output
            value:
              forecast: '{{forecastData}}'
              source: 'primary'

      # On error, try secondary service
      catch:
        - emit:
            event: weather.primary.failed
            data:
              error: '{{error}}'
              location: '{{event.data.parameters.location}}'

        # Try secondary weather service
        - try:
            do:
              - SecondaryWeatherAPI.getForecast:
                  location: '{{event.data.parameters.location}}'
                  days: '{{event.data.parameters.days || 3}}'
                  output: fallbackForecast

              # Return fallback forecast
              - set:
                  name: output
                  value:
                    forecast: '{{fallbackForecast}}'
                    source: 'secondary'

            # If secondary also fails, try simple location-based lookup
            catch:
              - emit:
                  event: weather.secondary.failed
                  data:
                    error: '{{error}}'
                    location: '{{event.data.parameters.location}}'

              # Provide basic message if all else fails
              - set:
                  name: output
                  value:
                    error:
                      code: 'ALL_PROVIDERS_FAILED'
                      message: 'All weather forecast providers failed'
                      userMessage: 'I was unable to retrieve the weather forecast at this time.'
                      suggestion: 'Please try again later or check a weather website directly.'
This pattern:
  • Attempts a primary approach first
  • Falls back to a secondary option if the primary fails
  • Escalates to a final simple backup method
  • Ensures the user receives a response even in failure scenarios
  • Communicates clearly which fallback was used

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Conclusion

Effective error handling in tool-using agents is not just a technical requirement—it’s a cornerstone of trust, usability, and resilience. By implementing structured strategies and reusable patterns, you can build agents that handle failure gracefully, communicate clearly with users, and improve continuously over time.
Learn how to monitor tool performance and capture feedback in the Monitoring & Analytics section.
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