This Langflow flow creates a ticket classification system that analyzes customer support messages. The flow receives support tickets through a chat input interface and processes them using an AI agent powered by OpenAI's GPT-4o model. The system is designed to automatically categorize incoming support requests without human intervention.

The core functionality centers around a detailed prompt template that instructs the AI to classify tickets on two dimensions: severity and sentiment. Severity levels range from Critical (urgent business-affecting issues) to Low (minor problems or general inquiries), while sentiment is categorized as Positive, Neutral, or Negative based on the customer's tone. The prompt includes specific examples and strict formatting requirements to ensure consistent output.

The AI agent processes each ticket according to these classification rules and returns structured results in a standardized format. The classified ticket information is then displayed through a chat output component, making it easy to view the results. This automated system helps support teams quickly prioritize and route tickets based on their urgency and the customer's emotional state.

For production deployments, tickets typically arrive via webhook nodes that receive POST requests from external ticketing systems, or through scheduled API requests that fetch new tickets from platforms like Zendesk or Jira Service Management. The flow uses data processing components to normalize ticket fields like subject, body, customer information, and communication channel before classification.

The language model component runs the classification instructions, often paired with structured output schemas to ensure consistent JSON formatting and reduce parsing errors. Logic components like conditional branches can route high-severity tickets to different processing paths, such as immediately alerting on-call teams for critical issues while sending routine requests to standard queues.

Results can be stored in databases, sent to notification systems, or used to update ticket fields in the original support platform through API calls. The playground environment allows teams to test classification accuracy with sample tickets before deploying the flow to production.

The flow can be extended significantly using other Langflow components. Embedding models and vector stores can match tickets to existing knowledge base articles for better context during classification. Agent components can automatically create follow-up tasks in project management tools or draft initial response templates. Database connectors enable storing classification results for analytics dashboards that track support trends over time. Teams can also chain multiple flows together, where the classification output triggers secondary workflows for customer sentiment analysis or automated response generation using MCP tools. Monitoring integrations with platforms like LangSmith provide visibility into classification accuracy and model performance over time.