Design Patterns for Context-Aware Conversational Agents in Enterprise Systems - Anup Rao (International Journal of Intelligent Systems and Applications in Engineering, 2023)
This study looked into how design patterns might improve the creation of conversational agents in enterprise systems that are aware of their context. Many conversational agents lacked contextual awareness, which limited their adaptability and resulted in inconsistent user experiences, even though they were increasingly being used in fields like corporate resource planning, human resources, and customer support. The study used a design science technique that included case study assessments, expert interviews, literature analysis, and prototype implementation in order to close this gap. A prototype agent was created by identifying, documenting, and incorporating a number of reusable design patterns, including Context Retention, Dynamic Role Adaptation, and Fallback Recovery. When compared to baseline systems, evaluation using expert validation, scenario-based testing, and performance measurement showed notable gains in context-switch handling, intent identification accuracy, reaction time, and user satisfaction. According to the results, design patterns provided a workable and scalable foundation for creating enterprise-level conversational bots that could manage intricate, multi-turn, and role-sensitive exchanges. In addition to offering reusable solutions for enterprise software development, this research made a theoretical contribution by formalizing patterns for conversational agent design.