Multi-Agent Customer Support Pipeline
Designed and deployed a multi-agent system that reduced customer support resolution time by 73% through intelligent ticket routing, context-aware response generation, and human-in-the-loop escalation.
73% faster resolution
Key Result
Tech Stack
Agent Pipeline
The Problem
A B2B SaaS company with 50,000+ active users was drowning in support tickets. Their existing system relied on keyword matching and manual routing, resulting in:
- 48-hour average resolution time, causing customer churn
- 35% misroute rate, with tickets bouncing between teams
- Agents spending 60% of their time on repetitive L1 queries that had documented answers
The VP of Customer Success needed a solution that could handle 70% of L1 tickets autonomously while maintaining quality standards for a regulated industry.
Architecture Decision
Instead of building a monolithic chatbot, I designed a multi-agent pipeline using LangGraph. Each node in the DAG has a single responsibility:
- Intake Agent: Normalizes incoming tickets, extracts metadata, classifies urgency
- Intent Router: Deterministic routing based on intent classification + confidence score
- RAG Retriever: Hybrid search (dense + sparse) across 15,000 knowledge base articles
- Resolution LLM: Generates grounded responses with citations
- Human Review: Catches edge cases with full conversation context
The critical design decision was making routing deterministic, not probabilistic. Low-confidence classifications always escalate to humans. No silent failures.
Implementation
RAG Pipeline
The retrieval layer uses a hybrid approach:
- Dense embeddings (text-embedding-3-large) for semantic search
- BM25 sparse index for exact keyword matching
- Cross-encoder re-ranking to combine results
- Citation extraction so every response links back to source documents
Agent Governance
Every agent decision is logged with:
- Input/output pairs
- Confidence scores
- Routing decisions with rationale
- Token usage and latency metrics
This creates a complete audit trail, critical for the client's compliance requirements.
Results
| Metric | Before | After | Impact | |--------|--------|-------|--------| | Avg Resolution Time | 48 hours | 13 hours | 73% reduction | | L1 Auto-Resolution | 0% | 68% | 68% automation | | Misroute Rate | 35% | 4% | 89% reduction | | CSAT Score | 3.2/5.0 | 4.6/5.0 | 44% improvement | | Monthly Cost | $180K | $95K | 47% cost savings |
The system now handles 2,400+ tickets per day with 99.97% uptime and sub-200ms response generation latency.
TL;DR
Designed and deployed a multi-agent system that reduced customer support resolution time by 73% through intelligent ticket routing, context-aware response generation, and human-in-the-loop escalation.