BOT REFLECTION

Enables agent self-analysis and improvement by using LLM to evaluate conversation quality, identify issues, and suggest improvements. This is a key feature for continuous agent optimization.

Syntax

BOT REFLECTION enabled
BOT REFLECTION ON "metric"
insights = BOT REFLECTION INSIGHTS()

Parameters

Description

BOT REFLECTION activates the agent self-improvement system, which periodically analyzes conversations and provides actionable insights. When enabled, the system:

• Analyzes conversation quality - Tone, clarity, helpfulness
• Identifies issues - Misunderstandings, incomplete answers, user frustration
• Suggests improvements - Better responses, missing information, tone adjustments
• Tracks metrics over time - Quality scores, resolution rates

This creates a continuous improvement loop where agents learn from their interactions.

Examples

Enable Basic Reflection

' Enable reflection for this bot session
BOT REFLECTION true

' Normal conversation proceeds
TALK "Hello! How can I help you today?"
HEAR userquery
response = LLM userquery
TALK response

' Reflection runs automatically in background

Monitor Specific Metrics

' Enable reflection on conversation quality
BOT REFLECTION ON "conversation_quality"

' Enable reflection on response accuracy
BOT REFLECTION ON "response_accuracy"

' Enable reflection on user satisfaction
BOT REFLECTION ON "user_satisfaction"

Retrieve Reflection Insights

' Get insights from reflection analysis
insights = BOT REFLECTION INSIGHTS()

IF insights <> "" THEN
    PRINT "Reflection Insights:"
    PRINT insights.summary
    PRINT "Quality Score: " + insights.qualityScore
    PRINT "Issues Found: " + insights.issuesCount
    
    FOR EACH suggestion IN insights.suggestions
        PRINT "Suggestion: " + suggestion
    NEXT suggestion
END IF

Use Insights for Self-Improvement

' Periodic reflection check
BOT REFLECTION true

' After conversation ends, check insights
insights = BOT REFLECTION INSIGHTS()

IF insights.qualityScore < 0.7 THEN
    ' Log for review
    PRINT "Low quality conversation detected"
    PRINT "Issues: " + insights.issues
    
    ' Store for analysis
    SET BOT MEMORY "reflection_" + conversationid, insights
END IF

Admin Dashboard Integration

' Script for admin to review bot performance
insights = BOT REFLECTION INSIGHTS()

BEGIN TALK
**Bot Performance Report**

📊 **Quality Score:** {insights.qualityScore}/1.0

📈 **Metrics:**
- Response Accuracy: {insights.responseAccuracy}%
- User Satisfaction: {insights.userSatisfaction}%
- Resolution Rate: {insights.resolutionRate}%

⚠️ **Issues Identified:**
{insights.issues}

💡 **Improvement Suggestions:**
{insights.suggestions}
END TALK

Conditional Reflection

' Only reflect on complex conversations
messageCount = GET BOT MEMORY("messageCount")

IF messageCount > 5 THEN
    ' Enable reflection for longer conversations
    BOT REFLECTION true
    BOT REFLECTION ON "conversation_quality"
END IF

Reflection with Alerts

' Enable reflection with alerting
BOT REFLECTION true

' Check for critical issues periodically
insights = BOT REFLECTION INSIGHTS()

IF insights.criticalIssues > 0 THEN
    ' Alert admin
    SEND MAIL admin, "Bot Alert: Critical Issues Detected", insights.summary
END IF

Reflection Metrics

Insights Object Structure

insights = BOT REFLECTION INSIGHTS()

' Available properties:
insights.qualityScore       ' Overall quality (0-1)
insights.summary           ' Text summary of analysis
insights.issues            ' Array of identified issues
insights.issuesCount       ' Number of issues found
insights.suggestions       ' Array of improvement suggestions
insights.metrics           ' Object with detailed metrics
insights.criticalIssues    ' Count of critical problems
insights.conversationId    ' ID of analyzed conversation
insights.timestamp         ' When analysis was performed

Config.csv Options

name,value
reflection-enabled,true
reflection-interval,10
reflection-min-messages,3
reflection-model,quality
reflection-store-insights,true

How Reflection Works

1. Collection - Conversation history is collected
2. Analysis - LLM analyzes the conversation against metrics
3. Scoring - Quality scores are calculated
4. Identification - Issues and patterns are identified
5. Suggestion - Improvement suggestions are generated
6. Storage - Results stored for dashboards and trends

Related Keywords

Performance Considerations

• Reflection uses LLM calls (affects cost/latency)
• Run reflection periodically, not on every message
• Use smaller models for reflection when possible
• Consider async reflection for production

Best Practices

1. Enable for complex bots - Most valuable for customer-facing agents
2. Review insights regularly - Use dashboards to spot trends
3. Act on suggestions - Update prompts and tools based on insights
4. Set appropriate intervals - Balance insight freshness vs cost
5. Store for analysis - Track improvements over time

Limitations

• Reflection adds LLM cost per analysis
• Analysis quality depends on model capability
• Cannot analyze real-time user emotions
• Historical only (not predictive)

See Also

• Multi-Agent Orchestration - Multi-agent systems
• Observability - Monitoring and metrics
• LLM Configuration - Model setup