Build a multi-step agent with LangGraph in Python that classifies tickets, routes them, and replies. Real code, state management, human-in-the-loop.

Agent that classifies incoming tickets, decides between auto-reply or human escalation, drafts the response, and logs to CRM.

Build a multi-step LangGraph agent for SaaS support

Define the graph State

TypedDict with fields the agent accumulates between nodes: ticket, classification, action, response, conversation_history.

Implement the classification node

Python node that calls Claude Haiku to classify the ticket into categories (billing, technical, general, urgent) and adds the classification to state.

Conditional routing based on classification

Conditional edge that routes to auto-respond for simple tickets or to human-review for urgent or complex tickets.

Response generation node

Call Claude Sonnet with a category-specific system prompt, optional RAG against internal docs, output with citations to the knowledge base.

Human-in-the-loop with interrupt

Set interrupt_before on the send node. When the agent wants to send something sensitive, it pauses and waits for human approval via endpoint.

Persistence and observability

Postgres checkpointer so the graph survives crashes. Structured logging of each state transition for debugging.

Articles about Python

LangGraph tutorial: your first multi-step agent for SaaS support automation