Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication

2025-10-22

AI Summary

To address the lack of task-oriented communication frameworks and low collaboration efficiency in multi-agent LLM systems, this paper proposes the “Communication-to-Completion” (C2C) framework. C2C introduces a novel task-alignment metric—quantified via the Alignment Factor (AF)—to measure goal consistency among agents, and designs a cost-aware sequential action mechanism enabling dynamic, on-demand communication decisions. By integrating stepwise execution, intelligent communication scheduling, and hierarchical collaboration modeling, C2C establishes, for the first time, a theoretical foundation for multi-agent communication effectiveness. Evaluated on three complex programming tasks, C2C reduces task completion time by approximately 40% compared to both non-communicative and fixed-step baselines, achieves a 100% end-to-end success rate, and demonstrates strong scalability.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Modeling collaborative workflows with intelligent multi-agent communication strategies
• Addressing lack of systematic frameworks for task-oriented communication in multi-agent systems
• Improving task completion efficiency through cost-aware dynamic communication decisions

Innovation

• Introduces Alignment Factor metric for agent task alignment
• Uses Sequential Action Framework for stepwise execution
• Enables cost-aware communication choices for efficiency