Multi-Agent AI Architectures for Automated Customer Service Management Systems
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P133Keywords:
Automated Customer Service Systems, Conversational AI Agents, Multi-Agent System Architectures, Dialogue Management Modules, Human–Agent Interaction Design, Natural Language Understanding (NLU), Multi-Turn Conversation Modeling, Intent Recognition Frameworks, Context-Aware Dialogue Systems, Speech and Non-Speech Behavior Analysis, Agent Communication Protocols, Task-Oriented Conversational Systems, Intelligent Module Coordination, Human-Like Virtual Assistants, Interaction Strategy Optimization, Distributed AI Agents, Perception-Driven Dialogue Processing, Service Automation Platforms, Cooperative Agent Architectures, AI-Based Client Support SystemsAbstract
An increasing number of companies have recently adopted automated systems to manage their Customer Service Department. Such systems typically consist of software applications capable of supporting operations such as online booking or client support. More recently, systems also include human-like conversational agents designed to manage tasks with users through spoken or written natural languages. More sophisticated systems support complex and multi-turn conversations thanks to the cooperation of several intelligent modules working together. These systems are usually designed as Multi-Agent Systems, where Artificial Intelligent modules called agents work together to achieve a specific objective. The growing complexity of dialogue-based tasks requires dialogue management modules to consider the agents’ speech, non-speech, and contextual behaviour to better infer users’ intents and to adopt appropriate strategies during human-agent interactions. Recent papers have proposed a complete architecture for this kind of system, putting special attention on the interaction among the agents during the task execution phase. The focus has been on defining the communication language between the agent and a set of modules that perceive the agent environment and recognize the user’s intent in natural language interactions. In these new architectures, the complexities in the processing of speech and contextual behaviour properties are delegated to these specific agents.
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