The flow begins by connecting to a SQLite database and executing a query to retrieve all billing records where the status is not "Paid". This data includes client information such as names, due dates, contact details, and current payment status. The SQL Database component handles the connection and query execution, returning structured data that can be processed downstream.

The retrieved records are then converted into a text format that can be processed by the AI agent. A Prompt Template component structures the instructions for the language model, defining how it should analyze the billing data and generate appropriate messages based on payment status.

An AI agent powered by OpenAI's language model analyzes the billing data and generates customized messages for each client. The agent system follows specific instructions to adjust the tone and urgency based on whether payments are pending or overdue. It has access to a calculator tool for any necessary computations and uses the current date to provide context for payment deadlines.

The final output is structured JSON data containing personalized messages for each client, along with their contact information and billing details. This structured format makes it easy to integrate with automated messaging systems or customer communication platforms through API endpoints. The system ensures that each message is appropriately tailored to the client's specific situation, whether they need a friendly reminder or a more urgent payment request.

The flow can be extended using additional Langflow components such as embedding models to analyze payment history patterns, API request nodes to integrate with external credit scoring services, or MCP server capabilities to connect with specialized financial tools. You could also add processing components to route different account types to specialized language models, or implement I/O components to create interactive dashboards for collection managers to review and approve messages before sending.