Customer Support Agent Playbook

Problem

Support teams handle 60-80% of tickets that are repetitive FAQs, draining agent time and creating inconsistent responses. As ticket volume scales, hiring linearly is unsustainable and new agents take weeks to ramp up on product knowledge.

Solution

Deploy a retrieval-augmented agent that indexes your help docs and product knowledge into a vector store, classifies inbound intent, drafts responses with tone guardrails, and escalates to humans when confidence is low.

Implementation Steps

1. Define support taxonomy

   Map your top 20 intents from historical tickets, required data sources for each, and escalation trigger rules.

   Tip: Start with your top 5 ticket categories — they likely cover 70% of volume.

2. Ingest support documentation

   Index FAQs, playbooks, product docs, and release notes into a vector store with metadata tags for freshness and source.

   # Chunk docs into ~500 token segments with overlap
   from langchain.text_splitter import RecursiveCharacterTextSplitter
   splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)

3. Build intent classifier

   Route each incoming query through an intent classifier to determine category, urgency, and required knowledge domain.

4. Build response orchestration

   Chain retrieval, drafting, and tone guardrails together. Use structured output to enforce response format consistency.

   Tip: Add a confidence score to every response — anything below 0.7 should route to a human.

5. Add escalation handoff

   When confidence drops below threshold or the customer requests a human, seamlessly transfer context to a live agent.

6. Monitor and improve

   Track resolution rate, CSAT, hallucination incidents, and cost per ticket. Use feedback loops to update the knowledge base weekly.

Recommended combos

FAQs