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Performance Tips

Configuration Strategy​

Understanding how to effectively configure AI agents requires following key principles that ensure consistent, reliable performance.

Start Simple, Build Complexity

Minimal Viable Configuration: Begin with the essential components—Identity, Speech Style, and Task—before adding advanced features. This approach allows for thorough testing of core functionality before introducing complexity.

Incremental Enhancement: Add workflow states, tools, and integrations gradually, validating each addition through comprehensive testing before proceeding to the next enhancement.

Practical Steps:

  1. Minimal Viable Agent: Begin with basic Identity, Speech Style, and Task
  2. Simple Workflow: Start with 1-2 states
  3. Core Functionality: Focus on primary use case first
  4. Test Thoroughly: Validate basic functionality before adding complexity
  5. Gradual Enhancement: Add features based on testing results
Specificity Over Abstraction

Concrete Instructions: Avoid vague descriptions like "speak professionally." Instead, provide specific guidance: "Use formal business language, address customers by title, and maintain a respectful but authoritative tone."

Behavioral Clarity: Define exact behaviors rather than abstract concepts. Replace "be helpful" with "provide step-by-step guidance, confirm understanding, and offer additional assistance proactively."

Consistency Across Components

Aligned Configuration: Ensure all configuration elements reinforce the same agent personality and approach. Identity, Speech Style, and Task should work together cohesively rather than creating conflicting directives.

Unified Voice: Maintain consistent tone and approach across all agent interactions, from initial greetings through complex problem-solving scenarios.

Deployment Strategy

Testing and Rollout Approach:

  1. Playground Testing: Extensive testing in controlled environment
  2. Limited Pilot: Deploy to small user group initially
  3. Monitor Performance: Track conversations and results closely
  4. Iterative Improvement: Refine based on real-world usage
  5. Scale Gradually: Expand to full deployment after validation

Communication and Style​

Regional and Cultural Considerations​

Be explicit about regional characteristics rather than assuming the LLM understands them.

❌ Ineffective: "Speak like a resident of Vologda region"
✅ Effective: "Use formal Russian with polite address forms. Speak respectfully and patiently, similar to customer service in traditional Russian banks."

Personality Consistency​

Ensure all configuration elements reinforce the same personality:

✅ Consistent Configuration:
Identity: "You are Sofia, a patient and understanding debt collection specialist"
Speech Style: "Speak with empathy and understanding. Use gentle language"
Task: "Collect payment using soft, respectful methods without pressure"

Handling Difficult Conversations​

De-escalation Techniques​

Include specific de-escalation instructions:

description: |
  If the customer becomes upset or hostile:
  1. Acknowledge their feelings: "I understand this is frustrating"
  2. Remain calm and professional
  3. Focus on solutions: "Let's see how we can resolve this"
  4. If escalation continues, transfer to human operator

Boundary Setting​

Clearly define what agents should and shouldn't do:

description: |
  ## You MUST do:
  - Be polite and respectful at all times
  - Follow data protection guidelines
  - Document all promises and agreements
  
  ## You MUST NOT do:
  - Make threats or use aggressive language
  - Promise things outside your authority
  - Share sensitive information without verification

Testing and Quality Assurance​

Playground Testing: Use the built-in Playground feature extensively to validate agent behavior across different scenarios before deployment.

Progressive Testing:

  • Start with happy path scenarios
  • Add edge cases gradually
  • Test different user personas
  • Validate error handling
  • Confirm integration functionality

Iterative Refinement: Continuously refine configuration based on testing results and real-world usage patterns. Monitor conversation quality and adjust settings to improve performance.

Comprehensive Testing Strategy

Follow these testing phases for optimal agent performance:

  1. Unit Testing: Test individual conversation elements
  2. Integration Testing: Test complete conversation flows
  3. Edge Case Testing: Test unusual or difficult scenarios
  4. Load Testing: Test performance under realistic usage
  5. User Acceptance Testing: Test with real users
Test Scenarios to Include
  • Happy Path: Ideal conversation flow
  • Confused Users: Unclear or unexpected responses
  • Difficult Customers: Hostile or uncooperative users
  • Edge Cases: Unusual situations or requirements
  • Technical Issues: System errors or integration failures
Playground Testing Approach
  1. Start Simple: Begin with basic conversation flows
  2. Gradually Increase Complexity: Add edge cases progressively
  3. Test Different Personas: Simulate various user types
  4. Document Issues: Keep track of problems and solutions
  5. Retest After Changes: Validate that fixes don't break other functionality
Creating Effective Test Scenarios
Example Test Cases:
1. Standard Collection Call:
   - Customer answers, confirms identity, agrees to payment plan
   
2. Wrong Number:
   - Different person answers, agent ends politely
   
3. Hostile Customer:
   - Customer becomes angry, agent de-escalates and transfers
   
4. Confused Customer:
   - Customer doesn't understand, agent clarifies patiently

Performance Optimization​

Response Time Optimization​

Factors affecting performance:

  • Knowledge Base Size: Larger knowledge bases take longer to search
  • Workflow Complexity: More states and transitions increase processing time
  • Integration Latency: External system calls add delay
  • Fast Access Knowledge: Too much always-available information slows responses

Optimization Strategies​

  1. Optimize Knowledge Base: Right-size chunk parameters for your content
  2. Simplify Workflows: Use fewer states when possible
  3. Cache Frequently Used Data: Store common information for quick access
  4. Monitor Integration Performance: Identify and resolve slow external calls

Resource Management​

Knowledge Base Management:

  • Split large knowledge bases into smaller, specialized ones
  • Regular cleanup of outdated information
  • Monitor usage patterns for optimization

Agent Configuration Efficiency:

  • Minimize Fast Access content to essentials only
  • Include only necessary tools in each state
  • Regular configuration review and cleanup

Monitoring and Continuous Improvement​

Regular Review Process​

Weekly Reviews:

  • Monitor active conversations and immediate issues
  • Review conversation quality and user satisfaction
  • Identify patterns in customer inquiries
  • Check system performance and error rates

Monthly Analysis:

  • Comprehensive conversation pattern analysis
  • Knowledge base usage and effectiveness review
  • Agent performance metrics evaluation
  • Integration performance and reliability assessment

Quarterly Optimization:

  • Strategic configuration review and updates
  • Knowledge base restructuring and optimization
  • Workflow refinement based on usage patterns
  • Performance optimization and scaling planning

Metrics to Track​

Conversation Quality:

  • User satisfaction scores
  • Conversation completion rates
  • Goal achievement rates
  • Escalation frequency and reasons

System Performance:

  • Average response times
  • Error rates and types
  • Integration reliability
  • Resource usage and efficiency

Business Impact:

  • Customer service efficiency improvements
  • Cost reduction through automation
  • Customer satisfaction improvements
  • Process optimization achievements