Multi-agent Application System in Office Collaboration Scenarios
This addresses office collaboration challenges for teams, but it appears incremental as it builds on existing multi-agent and AI technologies without claiming a major breakthrough.
The paper tackles the problem of improving office collaboration efficiency and work quality by introducing a multi-agent application system that integrates AI, ML, and NLP technologies, achieving functionalities like task allocation and progress monitoring, with validation showing outstanding performance in real business applications, particularly in query understanding, task planning, and tool calling.
This paper introduces a multi-agent application system designed to enhance office collaboration efficiency and work quality. The system integrates artificial intelligence, machine learning, and natural language processing technologies, achieving functionalities such as task allocation, progress monitoring, and information sharing. The agents within the system are capable of providing personalized collaboration support based on team members' needs and incorporate data analysis tools to improve decision-making quality. The paper also proposes an intelligent agent architecture that separates Plan and Solver, and through techniques such as multi-turn query rewriting and business tool retrieval, it enhances the agent's multi-intent and multi-turn dialogue capabilities. Furthermore, the paper details the design of tools and multi-turn dialogue in the context of office collaboration scenarios, and validates the system's effectiveness through experiments and evaluations. Ultimately, the system has demonstrated outstanding performance in real business applications, particularly in query understanding, task planning, and tool calling. Looking forward, the system is expected to play a more significant role in addressing complex interaction issues within dynamic environments and large-scale multi-agent systems.