Build an AI Customer Support Agent That Actually Works

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 Use: Letting the Agent Act

{ "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.