Overview

This LangGraph workflow implements a complete customer support pipeline. It uses a graph-based state machine to route tickets through classification, knowledge retrieval, response generation, and human escalation when needed.

Workflow Steps

  1. Ticket Intake: Receives customer query via API, email, or chat integration.
  2. Intent Classification: LLM-powered classifier categorizes the issue (billing, technical, account, general).
  3. Knowledge Retrieval: Searches internal knowledge base and documentation for relevant solutions.
  4. Response Generation: Drafts a contextual response using retrieved knowledge and conversation history.
  5. Confidence Check: Evaluates response quality; low-confidence answers route to human agent.
  6. Resolution Tracking: Logs resolution status, updates ticket system, and collects feedback.

Tools Required

  • LangGraph (Python framework for stateful agent workflows)
  • OpenAI API or Anthropic API for LLM capabilities
  • Vector database (Pinecone, Chroma, or pgvector) for knowledge retrieval
  • Ticketing system integration (Zendesk, Intercom, or custom API)

Use Cases

  • SaaS companies automating tier-1 support to reduce response time
  • E-commerce platforms handling order status, returns, and product questions
  • Internal IT help desks routing technical issues with automated solutions