A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs

TL;DR

This paper presents a practical framework for developing scalable, controllable, and reliable conversational agents using workflow graphs, addressing challenges in applying large language models to real-world industrial settings.

Contribution

It introduces a workflow-based approach to build production-grade conversational agents that balance flexibility and constraint compliance in industrial applications.

Findings

Effective strategies for integrating LLMs with strict service constraints

A case study demonstrating workflow optimization in e-commerce chatbot

Insights into bridging research and industrial deployment of conversational AI

Abstract

The advancement of Large Language Models (LLMs) has led to significant improvements in various service domains, including search, recommendation, and chatbot applications. However, applying state-of-the-art (SOTA) research to industrial settings presents challenges, as it requires maintaining flexible conversational abilities while also strictly complying with service-specific constraints. This can be seen as two conflicting requirements due to the probabilistic nature of LLMs. In this paper, we propose our approach to addressing this challenge and detail the strategies we employed to overcome their inherent limitations in real-world applications. We conduct a practical case study of a conversational agent designed for the e-commerce domain, detailing our implementation workflow and optimizations. Our findings provide insights into bridging the gap between academic research and real-world application, introducing a framework for developing scalable, controllable, and reliable AI-driven agents.