Agent Performance Summary

Documentation Index

Fetch the complete documentation index at: https://compass.docs.sunnyscoach.com/llms.txt

Use this file to discover all available pages before exploring further.

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Request

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Headers

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Path Parameters

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Parameter Format

• Format: YYYY-MM-DD (ISO 8601 date)
• Timezone: UTC
• Default: Last 7 days if not provided

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Response

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200 OK - Successfully retrieved agent high-level statistics

{
  "message": "",
  "data": {
    "agentAssignmentTimes": {
      "first": {
        "min": 1,
        "max": 45,
        "average": 8.5,
        "median": 6.2
      },
      "overall": {
        "min": 2,
        "max": 120,
        "average": 15.8,
        "median": 12.5
      }
    },
    "agentsCount": {
      "min": 1,
      "max": 5,
      "average": 1.8,
      "median": 1
    }
  }
}

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401 Unauthorized

{
  "error": {
    "code": "UNAUTHORIZED",
    "message": "Missing or invalid token"
  }
}

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500 Internal Server Error

{
  "error": {
    "code": "SERVER_ERROR",
    "message": "Internal server error"
  }
}

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Example

curl -X GET 'http://localhost:2000/email-meter/stats/agents-highlevel/2025-10-01/2025-10-07' \
  -H 'Authorization: oBearerBearer your-jwt-token'
'

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Data Fields Explained

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Agent Assignment Times

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Assignment Time Metrics

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Agents Count

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Performance Metrics

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Assignment Efficiency

• Excellent: < 5 minutes to first assignment
• Good: 5-10 minutes to first assignment
• Needs Improvement: 10-20 minutes
• Poor: > 20 minutes

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Agent Utilization

• Optimal: 1-2 agents per conversation
• Collaborative: 2-3 agents per conversation
• Overstaffed: > 3 agents per conversation
• Understaffed: Consistent 1 agent with long assignments

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Use Cases

• Team Performance: Monitor agent assignment efficiency
• Resource Planning: Optimize agent allocation
• Process Improvement: Identify bottlenecks in assignment
• Quality Assurance: Ensure timely agent responses
• Capacity Analysis: Understand agent workload distribution

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Analysis Examples

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Assignment Performance

const assignmentPerformance = {
  excellent: data.data.agentAssignmentTimes.first.average < 5,
  good: data.data.agentAssignmentTimes.first.average >= 5 && 
         data.data.agentAssignmentTimes.first.average < 10,
  needsImprovement: data.data.agentAssignmentTimes.first.average >= 10
};

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Workload Distribution

const workloadDistribution = {
  optimal: data.data.agentsCount.average <= 2,
  collaborative: data.data.agentsCount.average > 2 && 
                data.data.agentsCount.average <= 3,
  overstaffed: data.data.agentsCount.average > 3
};

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Best Practices

1. Monitoring: Track assignment times regularly
2. Alerts: Set up alerts for slow assignments
3. Balancing: Distribute workload evenly across agents
4. Training: Use data for agent performance training
5. Optimization: Continuously improve assignment processes

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Performance Targets

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Assignment Time Targets

• First Assignment: < 10 minutes (80% of conversations)
• Complete Assignment: < 20 minutes (90% of conversations)
• Maximum Time: < 60 minutes (99% of conversations)

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Agent Count Targets

• Average: 1.5-2.5 agents per conversation
• Maximum: < 5 agents per conversation
• Consistency: Low variance in agent counts

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Related Endpoints

• Use /email-meter/stats/agents-detailed for per-agent statistics
• Use /email-meter/stats/response-times for overall response metrics
• Use /email-meter/stats/resolved-times for resolution analysis

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Notes

• Assignment times are calculated from email receipt to agent assignment
• Times exclude automated assignments and routing
• Only human agent assignments are included
• Business hours are considered by default
• Weekends and holidays may affect average times