Orchestrating Conversational AI: Developing a Framework for Chatbot Lifecycle Management
Keywords:
Conversational AI, Chatbot Lifecycle, Dialogue Management, AI Orchestration, Natural Language Processing (NLP), Bot Development Framework, Conversational Design, Continuous ImprovementAbstract
As the adoption of conversational AI systems continues to expand across industries, there is a growing need for systematic approaches to manage the end-to-end lifecycle of chatbots. This paper proposes a comprehensive framework for chatbot lifecycle management that encompasses the design, development, deployment, monitoring, and continuous improvement of conversational agents. The framework addresses critical aspects such as intent recognition, dialogue flow orchestration, multi-platform integration, version control, and performance analytics. By aligning technical architecture with agile development and user-centric design principles, the framework enables organizations to scale chatbot operations while maintaining high levels of accuracy, usability, and adaptability. A case study and practical guidelines are included to demonstrate the framework’s effectiveness in real-world settings, offering valuable insights for developers, product managers, and AI strategists.
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