How do you build a customer service AI agent that handles complex support tickets?

A customer service AI agent combines intent classification, knowledge retrieval, and action execution. Architecture: ticket intake (classify priority, category, and sentiment), context gathering (retrieve customer history, account details, product information, similar resolved tickets), resolution attempt (search knowledge base via RAG, apply known solutions, execute account actions), and escalation (route to specialized human agents when confidence is low). Intent handling: implement a decision tree of common intents (billing inquiry, technical issue, feature request, complaint) with sub-intents (billing - refund, billing - charges, billing - plan change). Each intent maps to a resolution workflow with specific tools (account lookup, order management, knowledge base search). Conversation management: maintain context across multiple messages, handle topic switches gracefully, confirm understanding before taking actions, and provide status updates for long-running operations. Integration: CRM (Salesforce, HubSpot) for customer context, ticketing system (Zendesk, Jira Service Management) for ticket management, knowledge base for solution retrieval, and internal APIs for account actions. Quality assurance: implement CSAT survey after resolution, review conversations below confidence thresholds, and continuously fine-tune intent classification on new ticket data.