Building Production-Grade Conversational Agents with Workflow Graphs This paper presents a pragmatic, production-ready framework for building LLM-powered conversational agents using workflow graphs, with a specific focus on e-commerce scenarios. Instead of relying solely on end-to-end generation, the authors design agents using a directed acyclic graph (DAG), enabling flexible yet controllable interactions that adhere to strict business rules and format constraints.

Multi-State DAG Framework: Each node in the graph corresponds to a conversational state with its own system prompt, tool access, and execution rules. This structure enables robust constraint handling (e.g., avoiding hallucinated responses or non-compliant suggestions) by localizing logic and formatting within specific graph nodes.

Fine-Tuning via Response Masking: Because conversation turns come from different states in the DAG, the authors introduce a fine-tuning strategy that applies selective loss masking to train LLMs only on responses relevant to a specific node’s context. This prevents prompt conflicts and improves adherence to node-specific constraints.

Real-World Deployment and Results: In a deployment across KakaoTalk and web platforms, the graph-based approach significantly outperformed baseline agents and even GPT-4o across key metrics like task accuracy (+52%) and format adherence (+50%). In human preference tests, their internal model was favored over GPT-4o in 63% of real-world user cases, especially in product recommendation and safety-critical tasks.