Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication
This addresses the problem of inefficient teamwork in multi-agent systems for complex collaborative tasks, representing a novel method rather than an incremental improvement.
The paper tackled the lack of systematic communication frameworks in multi-agent LLM systems for complex tasks by introducing the Communication to Completion (C2C) framework, which reduced task completion time by about 40% in coding workflows with 5 to 17 agents.
Teamwork in workspace for complex tasks requires diverse communication strategies, but current multi-agent LLM systems lack systematic frameworks for task oriented communication. We introduce Communication to Completion (C2C), a scalable framework that addresses this gap through two key innovations: (1) the Alignment Factor (AF), a novel metric quantifying agent task alignment that directly impacts work efficiency, and (2) a Sequential Action Framework that integrates stepwise execution with intelligent communication decisions. C2C enables agents to make cost aware communication choices, dynamically improving task understanding through targeted interactions. We evaluated C2C on realistic coding workflows across three complexity tiers and team sizes from 5 to 17 agents, comparing against no communication and fixed steps baselines. The results show that C2C reduces the task completion time by about 40% with acceptable communication costs. The framework completes all tasks successfully in standard configurations and maintains effectiveness at scale. C2C establishes both a theoretical foundation for measuring communication effectiveness in multi-agent systems and a practical framework for complex collaborative tasks.