Common Issues and Solutions - AgentKit Troubleshooting Guide¶

Check List:
  - Agent deployment status: Active/Inactive/Error
  - Resource allocation: CPU, Memory, API limits
  - Network connectivity: Internet, API endpoints
  - Service dependencies: All required services running

Timeline Analysis:
  - When did the agent last work correctly?
  - What changes were made recently?
  - Any system updates or configuration changes?
  - External service disruptions or maintenance?

Problem: Agent running out of memory or CPU
Symptoms: Gradual slowdown, then complete stoppage

Solution:
  1. Check resource usage in AgentKit dashboard
  2. Increase agent resource allocation:
     - Memory: Upgrade to next tier
     - CPU: Enable auto-scaling
     - API Limits: Review and increase quotas
  3. Optimize agent processing:
     - Reduce concurrent operations
     - Implement batching for large datasets
     - Add caching for repeated operations

Problem: Hitting rate limits on external APIs
Symptoms: Intermittent failures, "rate limit exceeded" errors

Solution:
  1. Identify rate-limited APIs in logs
  2. Implement rate limiting strategies:
     - Add delays between API calls
     - Implement exponential backoff
     - Use multiple API keys if available
  3. Optimize API usage:
     - Cache responses when possible
     - Batch API requests
     - Reduce polling frequency

Problem: Invalid configuration preventing startup
Symptoms: Agent fails to start or initialize

Solution:
  1. Validate agent configuration:
     - Check JSON/YAML syntax
     - Verify all required fields
     - Confirm data type accuracy
  2. Review integration settings:
     - API endpoints and URLs
     - Authentication credentials
     - Permission scopes
  3. Reset to known working configuration:
     - Restore from backup
     - Use default template
     - Rebuild configuration step by step

Check Authentication:
  - API keys: Valid and not expired
  - OAuth tokens: Not expired, proper scopes
  - Certificates: Not expired, properly installed
  - IP whitelist: AgentKit IPs approved

External Service Health:
  - Check service status pages
  - Review API documentation for changes
  - Test with API testing tools (Postman)
  - Contact service provider support

Problem: OAuth access tokens expired
Symptoms: "Unauthorized" or "Invalid token" errors

Solution:
  1. Check token expiration:
     - Review token metadata
     - Check expiration timestamps
     - Verify refresh token validity
  2. Implement automatic token refresh:
     - Set up refresh token automation
     - Add token expiration monitoring
     - Implement graceful re-authentication
  3. Update authentication configuration:
     - Re-authorize applications
     - Update stored credentials
     - Test authentication flow

Problem: External API updated breaking compatibility
Symptoms: "Invalid request" or "Method not found" errors

Solution:
  1. Identify API version changes:
     - Review API documentation
     - Check changelog and breaking changes
     - Test with updated API endpoints
  2. Update integration code:
     - Modify request formats
     - Update response parsing
     - Handle new error conditions
  3. Implement version management:
     - Use versioned API endpoints
     - Monitor for deprecation notices
     - Test with multiple API versions

Problem: Network security blocking connections
Symptoms: Connection timeouts, "Connection refused" errors

Solution:
  1. Check firewall rules:
     - Verify outbound connections allowed
     - Confirm ports and protocols
     - Check IP whitelist requirements
  2. Configure security settings:
     - Add AgentKit IPs to whitelists
     - Configure proxy settings if required
     - Update SSL/TLS certificates
  3. Work with IT/Security teams:
     - Request firewall exceptions
     - Provide security documentation
     - Implement security compliance

Review Required Permissions:
  - List all agent operations
  - Document required permission scopes
  - Compare with granted permissions
  - Identify missing or insufficient scopes

Permission Types:
  User Permissions:
    - Individual user account limits
    - Role-based access controls
    - Department or team restrictions

  Application Permissions:
    - API application scopes
    - System-level access rights
    - Service account permissions

Steps to Fix:
  1. Review API documentation for required scopes
  2. Request additional permissions:
     - Update OAuth application registration
     - Request admin approval for broader scopes
     - Re-authenticate with new permissions
  3. Test permission grants:
     - Verify each operation works
     - Document successful permission sets
     - Create permission templates

Steps to Fix:
  1. Identify user role requirements:
     - Document minimum role needed
     - Request role elevation if necessary
     - Create dedicated service accounts
  2. Configure service accounts:
     - Create accounts with appropriate roles
     - Grant minimum necessary permissions
     - Document permission rationale

Metrics to Track:
  - Average response time by operation type
  - 95th percentile response times
  - Response time trends over time
  - Correlation with system load

Common Bottleneck Areas:
  - API call latency
  - Data processing complexity
  - Database query performance
  - Network connectivity issues

Optimizations:
  1. Implement caching:
     - Cache frequently accessed data
     - Use appropriate cache expiration
     - Implement cache invalidation
  2. Reduce API calls:
     - Batch multiple requests
     - Use bulk operations when available
     - Minimize polling frequency
  3. Parallel processing:
     - Execute independent operations concurrently
     - Use async/await patterns
     - Implement operation queuing

Improvements:
  1. Optimize algorithms:
     - Review processing logic
     - Implement more efficient algorithms
     - Reduce data transformation overhead
  2. Stream processing:
     - Process data in chunks
     - Use streaming APIs
     - Implement incremental processing
  3. Resource allocation:
     - Increase processing power
     - Optimize memory usage
     - Use specialized processing agents

Error Types:
  Transient Errors:
    - Network timeouts
    - Rate limiting
    - Temporary service unavailability

  Permanent Errors:
    - Invalid configuration
    - Permission denied
    - Malformed requests

  Data Errors:
    - Invalid input format
    - Missing required fields
    - Data validation failures

Investigation Steps:
  1. Error pattern analysis:
     - When do errors occur?
     - What operations are affected?
     - Are errors correlated with specific inputs?
  2. System correlation:
     - System load during errors
     - External service status
     - Configuration changes
  3. Data quality assessment:
     - Input validation
     - Data format consistency
     - Edge case handling

Implementation:
  1. Implement retry logic:
     - Exponential backoff for transient errors
     - Maximum retry limits
     - Circuit breaker patterns
  2. Enhanced validation:
     - Input validation before processing
     - Data format verification
     - Range and constraint checking
  3. Graceful degradation:
     - Fallback operations
     - Partial success handling
     - User-friendly error messages

Monitoring Setup:
  1. Real-time alerting:
     - Error rate thresholds
     - Performance degradation alerts
     - Service dependency monitoring
  2. Predictive analytics:
     - Error pattern prediction
     - Capacity planning
     - Maintenance scheduling

Analysis Points:
  - Step completion times
  - Queue lengths by step
  - Agent utilization rates
  - Throughput by workflow stage

Capacity Assessment:
  - Current vs. required capacity
  - Peak load handling
  - Scalability limitations
  - Resource allocation efficiency

Implementation:
  1. Distribute workload:
     - Implement round-robin routing
     - Use weighted distribution
     - Consider agent specialization
  2. Auto-scaling:
     - Dynamic agent provisioning
     - Queue-based scaling triggers
     - Resource optimization

Optimizations:
  1. Parallel processing:
     - Identify independent operations
     - Implement concurrent execution
     - Reduce sequential dependencies
  2. Workflow redesign:
     - Eliminate unnecessary steps
     - Combine related operations
     - Optimize data flow

Metrics to Track:
  - Classification accuracy rate
  - False positive/negative rates
  - User satisfaction with decisions
  - Manual override frequency

Analysis Areas:
  - Decision consistency across similar inputs
  - Edge case handling
  - Bias in decision-making
  - Learning from feedback

Improvements:
  1. Expand training examples:
     - Add more diverse examples
     - Include edge cases
     - Balance training data
  2. Improve data quality:
     - Clean inconsistent examples
     - Add context information
     - Verify example accuracy
  3. Continuous learning:
     - Implement feedback loops
     - Regular training updates
     - Performance monitoring

Enhancements:
  1. Improve decision criteria:
     - Clarify decision rules
     - Add contextual factors
     - Implement weighted scoring
  2. Add validation steps:
     - Multi-factor validation
     - Confidence scoring
     - Human review triggers

Standardization Areas:
  - Message templates and formats
  - Tone and personality consistency
  - Information accuracy and completeness
  - Response timing and structure

Quality Measures:
  - Response template compliance
  - Brand voice adherence
  - Accuracy verification
  - User experience consistency

Implementation:
  1. Develop response templates:
     - Create templates for common scenarios
     - Include brand voice guidelines
     - Provide variation examples
  2. Template enforcement:
     - Automated template checking
     - Quality scoring systems
     - Feedback and correction loops

Process Implementation:
  1. Regular quality reviews:
     - Sample response analysis
     - User feedback integration
     - Performance trend monitoring
  2. Continuous improvement:
     - Template updates based on performance
     - Training data refinement
     - Best practice documentation

Data Sources:
  - Direct satisfaction surveys
  - User behavior analytics
  - Support ticket feedback
  - Social media monitoring

Analysis Framework:
  - Response quality assessment
  - Issue resolution effectiveness
  - User experience journey mapping
  - Expectation vs. reality gaps

Actions:
  1. Personalization:
     - Use customer history and preferences
     - Adapt communication style
     - Provide relevant recommendations
  2. Proactive service:
     - Anticipate customer needs
     - Provide helpful suggestions
     - Follow up on resolutions
  3. Escalation improvement:
     - Clear escalation triggers
     - Smooth handoff processes
     - Context preservation

Implementation:
  1. Real-time feedback collection:
     - Post-interaction surveys
     - Inline feedback options
     - Behavior tracking
  2. Rapid response to issues:
     - Daily satisfaction monitoring
     - Quick issue resolution
     - Proactive improvement implementation

Key Metrics to Monitor:
  Performance:
    - Response times (target: <2 seconds)
    - Throughput (requests per minute)
    - Error rates (target: <2%)
    - Availability (target: 99.9%)

  Quality:
    - Accuracy rates (target: >95%)
    - Customer satisfaction (target: >4.5/5)
    - Escalation rates (target: <10%)
    - Resolution rates (target: >85%)

  Business Impact:
    - Cost per interaction
    - Time savings achieved
    - Process efficiency gains
    - User adoption rates

Alert Levels:
  Critical (Immediate Response):
    - System down or unresponsive
    - Error rates >10%
    - Customer satisfaction <3.0
    - Security breaches

  Warning (1-hour Response):
    - Performance degradation
    - Error rates 5-10%
    - Queue backlogs
    - Integration issues

  Info (Daily Review):
    - Performance trends
    - Usage patterns
    - Optimization opportunities
    - Capacity planning needs

Daily:
  - Performance metrics review
  - Error log analysis
  - Queue monitoring
  - User feedback review

Weekly:
  - System health assessment
  - Performance optimization
  - Training data updates
  - Process improvements

Monthly:
  - Comprehensive system review
  - Capacity planning
  - Security assessments
  - User satisfaction analysis

Quarterly:
  - Strategic performance review
  - Technology updates
  - Process redesign
  - Training and development

Change Protocol:
  1. Testing in staging environment
  2. Gradual rollout to production
  3. Performance monitoring during deployment
  4. Rollback procedures if issues arise
  5. Post-deployment validation
  6. Documentation updates

Documentation Requirements:
  - Step-by-step problem resolution guides
  - Common issue patterns and solutions
  - Contact information for escalations
  - System architecture and dependencies
  - Recovery procedures and runbooks

Knowledge Management:
  - Regular team training sessions
  - Issue post-mortems and lessons learned
  - Best practice documentation
  - Cross-team communication protocols

Severity Levels:
  P1 (Critical): System completely down, customer impact severe
  P2 (High): Significant functionality impaired, workarounds available
  P3 (Medium): Minor functionality issues, minimal customer impact
  P4 (Low): Cosmetic issues, no functional impact

Response Time Requirements:
  P1: 15 minutes acknowledgment, 1 hour initial response
  P2: 30 minutes acknowledgment, 2 hours initial response
  P3: 1 hour acknowledgment, 4 hours initial response
  P4: 24 hours acknowledgment, next business day response

Level 1: Agent administrator/developer
Level 2: Technical team lead
Level 3: Engineering manager
Level 4: AgentKit support team
Level 5: Executive escalation

AgentKit Support:
  - Support portal and ticket system
  - Community forums and documentation
  - Enterprise support channels
  - Professional services and consulting