🤖 AI Summary
Large language models (LLMs) exhibit unstable dialogue behavior and poor maintainability in complex business processes. Method: This paper proposes Conversation Routines (CR), a framework that formalizes task-oriented dialogue logic via natural-language specifications, pioneering the integration of structured business workflows directly into LLM prompts—thereby decoupling dialogue design from tool implementation. CR supports modular routine definition and composition, natural-language-driven workflow orchestration, and synergistically combines tool-augmented conversational agents (Tool-Augmented CAS) with prompt engineering. Contribution/Results: Evaluated on two proof-of-concept scenarios—train ticket booking and interactive fault diagnosis—CR enables domain experts to build high-fidelity, high-task-success-rate dialogues without coding. It significantly improves system interpretability, reusability, and cross-role collaboration efficiency.
📝 Abstract
This study introduces Conversation Routines (CR), a structured prompt engineering framework for developing task-oriented dialog systems using Large Language Models (LLMs). While LLMs demonstrate remarkable natural language understanding capabilities, engineering them to reliably execute complex business workflows remains challenging. The proposed CR framework enables the development of Conversation Agentic Systems (CAS) through natural language specifications, embedding task-oriented logic within LLM prompts. This approach provides a systematic methodology for designing and implementing complex conversational workflows while maintaining behavioral consistency. We demonstrate the framework's effectiveness through two proof of concept implementations: a Train Ticket Booking System and an Interactive Troubleshooting Copilot. These case studies validate CR's capability to encode sophisticated behavioral patterns and decision logic while preserving natural conversational flexibility. Results show that CR enables domain experts to design conversational workflows in natural language while leveraging custom enterprise functionalities (tools) developed by software engineers, creating an efficient division of responsibilities where developers focus on core API implementation and domain experts handle conversation design. While the framework shows promise in accessibility and adaptability, we identify key challenges including computational overhead, non-deterministic behavior, and domain-specific logic optimization. Future research directions include enhancing system robustness, improving scalability for complex multi-agent interactions, and addressing the identified limitations across diverse business applications.