Communication to Completion: Modeling Collaborative Workflows with
  Intelligent Multi-Agent Communication
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.
68fb803d6cdff8b857f46b4d
Yiming Lu
68fb803d6cdff8b857f46b4e
Xun Wang
68fb803d6cdff8b857f46b4f
Simin Ma
68fb803d6cdff8b857f46b50
Shujian Liu
68fb803d6cdff8b857f46b51
Sathish Reddy Indurthi
68fb803d6cdff8b857f46b52
Song Wang
68fb803d6cdff8b857f46b53
Haoyun Deng
68fb803d6cdff8b857f46b54
Fei Liu
68fb803d6cdff8b857f46b55
Kaiqiang Song
Communication to Completion: Modeling Collaborative Workflows with
  Intelligent Multi-Agent Communication
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.
C2C, a scalable framework for multi-agent LLM systems, improves task completion time through the Alignment Factor and Sequential Action Framework, enabling cost-aware communication and dynamic task understanding.
multi-agent LLM systems Communication to Completion (C2C) Alignment Factor (AF) Sequential Action Framework task completion time communication costs task understanding coding workflows