Why Most AI Support Bots Fail
Most companies bolt a chatbot onto their help center and call it AI support. Customers get canned answers, loop endlessly, and churn. A real AI support agent is different.
The Three-Pillar Architecture
| Pillar | Purpose | Key Tech |
|---|
| RAG | Ground answers in your docs | Vector DB + embedding model |
| Tool Use | Take action (refunds, status) | Function calling / MCP |
| Escalation | Know when to hand off | Confidence scoring |
RAG: Grounding in Your Knowledge Base
User query -> Embed -> Vector search -> Top-K docs -> LLM prompt
- Chunk docs at ~500 tokens with 50-token overlap
- Use metadata filters (product, language, recency)
- Always cite the source document in the response
{ "tool": "check_order_status", "params": { "order_id": "ORD-9281" } }
Smart Escalation
Escalate when confidence < 0.6, customer is frustrated, query involves billing disputes, or the agent has looped twice.
Track resolution rate, CSAT, and escalation rate. Aim for 70%+ autonomous resolution.