Communication to Completion: Modeling Collaborative Workflows with
  Intelligent Multi-Agent Communication
Authors: name: Yiming Lu, name: Xun Wang, name: Simin Ma, name: Shujian Liu, name: Sathish Reddy Indurthi, name: Song Wang, name: Haoyun Deng, name: Fei Liu, name: Kaiqiang Song

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.