Overview
Multi-agent customer support systems replace monolithic chatbots with coordinated teams of specialized agents. Like human support organizations, these systems route inquiries to the most appropriate agent, escalate complex issues, and maintain quality standards.
Architecture
Customer → Triage Agent → Route to Specialist
↓
┌───────┴───────┐
↓ ↓ ↓
Billing Technical Account
Agent Agent Agent
└───────┬───────┘
↓
Resolution or Escalation
↓
QA Agent (monitors quality)
Agent Roles
Triage Agent
- Classifies incoming requests by intent and urgency
- Extracts key information (account ID, product, issue type)
- Routes to appropriate specialist
- Handles simple queries directly
Specialist Agents
- Billing Agent: Invoices, payments, refunds, plan changes
- Technical Agent: Product issues, troubleshooting, bugs
- Account Agent: Profile changes, security, access issues
Escalation Agent
- Handles cases beyond specialist capability
- Coordinates with human supervisors
- Manages SLA-critical situations
QA Agent
- Monitors conversation quality in real-time
- Flags potential issues for review
- Provides coaching feedback to other agents
Real-World Results
Klarna (2024): AI assistant handled 2.3M conversations in first month, reducing resolution time from 11 minutes to under 2 minutes, equivalent to 700 FTE.
Intercom Fin: Achieves ~51% automated resolution rate. Synthesia saved 1,300+ support hours in 6 months.
European Telecom: 60% resolution time reduction, €1M+ annual savings, significant NPS improvement.
Key Patterns Used
- Supervisor Pattern: Central routing and orchestration
- Handoff Pattern: Clean transitions between specialists
- Human-in-the-Loop: Escalation to human agents
- LLM-as-Judge: QA monitoring
Common Failure Modes
- Context Drift: Customer history lost between handoffs
- Infinite Handoff Loop: Agents bounce customer between specialists
- Capability Spoofing: Routing to wrong specialist
- Cascading Failures: Triage errors propagate through system