Generative Agents at Scale: A Practical Guide to Migrating from Dialog Trees to LLM Frameworks
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805010Keywords:
Generative AI, Large Language Models, Digital Virtual Agents, Conversational AI, Migration Strategy, Dialog Management, Enterprise AI, Healthcare IT, FinTech, Telecommunications, AI Deployment, Practical Guide, IEEE StandardsAbstract
The rapid advancements in Large Language Models (LLMs) are fundamentally transforming conversational AI, enabling the development of more flexible, context-aware, and human-like Digital Virtual Agents (DVAs). While traditional dialog tree-based systems have served as the backbone for many enterprise chatbots, their inherent rigidity and maintenance overhead limit scalability and user experience in complex scenarios. Migrating from these rule-based systems to LLM-driven frameworks presents significant technical and operational challenges, particularly at scale and within highly regulated industries like healthcare, finance, and telecommunications. This paper provides a practical guide for organizations undertaking this migration. We analyze the limitations of dialog trees, the capabilities introduced by LLMs, and propose a structured migration methodology encompassing assessment, planning, phased implementation, testing, and ongoing governance. The paper details practical strategies for leveraging LLM frameworks while maintaining control, ensuring accuracy, managing security and privacy, and integrating with existing enterprise systems. We discuss industry-specific considerations, drawing insights from real-world challenges and widely adopted approaches in healthcare, finance, and telecom. The aim is to equip practitioners with a clear understanding of the steps, considerations, and technical approaches necessary to successfully transition to scalable, intelligent, and effective generative agent architectures.
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