Customer Escalation Multi-Agent System

AI-Powered Escalation Command

Real-time monitoring and automated resolution of customer escalations across Kenya's enterprise customer base using multi-agent AI architecture.

Groq Llama-3.1-8B Multi-Agent System

+12%

Total Complaints Today

1,247

+5%

Active Escalations

89

CRITICAL

Critical (L4) Cases

12

-8%

Avg Resolution Time

4.2h

High Risk

Churn Risk Customers

34

Escalations by Level

Complaints by Department

Sentiment Distribution

Positive 32% Neutral 28% Negative 40%

Escalation Trend (7 Days)

Recent AI Resolutions

L2 Escalation Auto-Resolved

Customer: James Mwangi • Billing Issue

2 minutes ago

Response Agent

Routed to Technical Team

Customer: Sarah Ochieng • Network Issue

5 minutes ago

Decision Agent

L4 Critical Escalation Detected

Customer: David Kimani • Service Outage

8 minutes ago

Detection Agent

Live Feed Updated: Just now

Complaint Input

Customer ID

Complaint Text

AI Analysis

Enter a complaint and click analyze to see AI insights

AI Response Draft

Generated by Response Agent

RAG Retrieved Context

SIMILAR CASE #1

"Double billing issue - customer charged for same service period"

Resolution: Full refund + 1 month free credit

Similarity: 94%

SIMILAR CASE #2

"Premium service billing confusion - customer unaware of charges"

Resolution: Service downgrade + prorated refund

Similarity: 87%

KNOWLEDGE BASE

Billing Dispute Resolution Policy v2.3

Auto-approve refunds under KES 10,000 for Gold+ tier

Relevance: 91%

Multi-Agent Decision Flow

Transparent view into AI agent reasoning and decision-making process

1

Detection Agent

Identifies complaint type and initial severity

Completed

PROMPT SUMMARY

Analyze customer complaint for: 1) Issue category 2) Emotional intensity 3) Urgency indicators 4) Business impact keywords

AI REASONING

Detected keywords: "outage", "business critical", "losing money". Emotional intensity: HIGH. Urgency: IMMEDIATE. Category: Service Disruption.

JSON DECISION OUTPUT

{
  "complaint_type": "service_outage",
  "severity_score": 0.89,
  "emotional_intensity": "high",
  "urgency": "immediate",
  "business_impact": true,
  "detected_entities": ["internet", "business", "outage", "money"]
}

2

Decision Agent

Routes to appropriate department and sets escalation level

Completed

PROMPT SUMMARY

Based on detection output, determine: 1) Escalation level (L1-L4) 2) Department routing 3) SLA priority 4) Compensation eligibility

AI REASONING

High severity + business impact + immediate urgency = L4 Critical. Route to Technical Operations. SLA: 1 hour. Customer tier: Gold = eligible for service credit.

JSON DECISION OUTPUT

{
  "escalation_level": "L4",
  "department": "technical_operations",
  "sla_priority": "P1",
  "sla_time": "1_hour",
  "compensation_eligible": true,
  "compensation_type": "service_credit",
  "compensation_amount": "KES 5,000",
  "requires_human": true
}

3

Response Agent

Generates personalized response with compensation offer

Completed

PROMPT SUMMARY

Generate empathetic response acknowledging issue, explaining actions taken, offering compensation, and setting clear expectations.

AI REASONING

Customer is Gold tier with business impact. Tone: apologetic but confident. Include specific timeline. Offer service credit as approved by Decision Agent.

GENERATED RESPONSE

Dear Wanjiku,

I sincerely apologize for the service outage affecting your business operations. Our Technical Operations team has been notified and is treating this as our highest priority (P1).

Immediate Actions:
• Engineering team dispatched to your area
• Estimated resolution: Within 1 hour
• Direct hotline: +254 720 123 456

Service Recovery:
As a valued Gold customer, we're applying a KES 5,000 service credit to your account, effective immediately.

I'll personally monitor this case and update you every 30 minutes until resolved.

Best regards,
AI Support Team

Memory & Conversation History

Persistent customer memory across all interactions

Memory Summary

PREFERRED CHANNEL

COMMUNICATION STYLE

Direct, business-focused

Prefers concise responses

PAIN POINTS

Billing Network Speed

SATISFACTION HISTORY

Wanjiku Muthoni

Last active: 2 hours ago

Analytics & Risk Insights

Kenya-wide customer escalation intelligence

Top Customers by Escalations

Escalations by City (Kenya)

Churn Risk Heatmap by Tier & Time

00-04

04-08

08-12

12-16

16-20

20-24

Avg

Customer Tier vs Escalation Frequency

High Risk Alert

Nairobi CBD Network

12 customers in 100m radius reported outages. Potential infrastructure issue.

Trending Pattern

Billing Disputes +23%

Gold tier customers showing increased billing confusion after new pricing rollout.

AI Success

Auto-Resolution 78%

L1-L2 escalations successfully resolved without human intervention this week.

Executive Dashboard

AI-Powered Escalation Command

Escalations by Level

Complaints by Department

Sentiment Distribution

Escalation Trend (7 Days)

Recent AI Resolutions

Complaint Input

AI Analysis

AI Response Draft

Wanjiku Muthoni

Customer Metrics

Recent Interactions

RAG Retrieved Context

Multi-Agent Decision Flow

Detection Agent

Decision Agent

Response Agent

Memory & Conversation History

Memory Summary

Analytics & Risk Insights

Top Customers by Escalations

Escalations by City (Kenya)

Churn Risk Heatmap by Tier & Time

Customer Tier vs Escalation Frequency