Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}

本文介绍了一种结合了学术和工业界专业知识的神经代理助手（NAA）框架，具有意图识别、上下文检索和响应生成三个高级组件，旨在帮助改善客户服务支持。通过三个案例研究，证明了应用这一框架是解决独特挑战的有效途径，同时建议推动协同创新，促进工业界交谈式AI任务的NLP模型的发展。

将最先进技术带给客户：面向客户服务支持的神经代理辅助框架