Error Handling

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.

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

Error Categories in Tool-Using Agents

Tool-using agents can encounter several categories of 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

Error Handling Strategy

A comprehensive error handling strategy includes several key components:

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

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

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

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

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

Implementing Error Handling in Prisme.ai

Prisme.ai provides several mechanisms for implementing robust error handling:

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

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

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.

Getting Started

Prompting & RAG Agent

Tool-Using Agents

The Importance of Error Handling

Reliability

User Experience

Trust

Maintainability

Resilience

Visibility

Error Categories in Tool-Using Agents

Error Handling Strategy

Implementing Error Handling in Prisme.ai

Error Handling Patterns

Conclusion

Getting Started

Prompting & RAG Agent

Tool-Using Agents

​The Importance of Error Handling

Reliability

User Experience

Trust

Maintainability

Resilience

Visibility

​Error Categories in Tool-Using Agents

​Error Handling Strategy

​Implementing Error Handling in Prisme.ai

​Error Handling Patterns

​Conclusion

The Importance of Error Handling

Error Categories in Tool-Using Agents

Error Handling Strategy

Implementing Error Handling in Prisme.ai

Error Handling Patterns

Conclusion