让AI代理像外包工人一样互换任务，省下20%算力成本。
COALESCE: Economic and Security Dynamics of Skill-Based Task Outsourcing Among Team of Autonomous LLM Agents
- 用技能评估动态分配任务，按需外包给更便宜的AI代理。
- 实测降低20.3%运行成本，理论可省41.8%。
- 适合想降本增效的AI系统开发者和部署团队。
自主大型语言模型（LLM）代理的迅猛发展带来了广泛应用潜力，但其部署受限于对图形处理器（GPU）资源的巨大需求。本文提出COALESCE（基于技能评估的成本优化与安全代理劳动交换），一个使自主LLM代理能动态将特定子任务外包给专业化、低成本第三方代理的新框架。该框架融合混合技能表示、动态技能发现、自动任务分解、统一成本模型（比较内部执行成本与外部外包价格）、简化的基于市场的决策算法，以及代理间标准化通信协议。通过239次理论模拟验证，潜在成本降低41.8%；在240个真实任务的大规模实证测试中，采用适当ε-贪心探索策略，实现20.3%的成本下降，证明了其理论可行性与实际有效性。随着谷歌等提出的Agent2Agent（A2A）等开放标准的发展，COALESCE框架能有效利用此类协议促进高效代理协作。通过构建动态代理能力市场，可能借助如A2A的通信协议，显著降低运营成本，提升系统可扩展性，推动专业化代理经济生态形成，使复杂LLM代理功能更具经济可行性和可访问性。
原文摘要 · Abstract (English)
The meteoric rise and proliferation of autonomous Large Language Model (LLM) agents promise significant capabilities across various domains. However, their deployment is increasingly constrained by substantial computational demands, specifically for Graphics Processing Unit (GPU) resources. This paper addresses the critical problem of optimizing resource utilization in LLM agent systems. We introduce COALESCE (Cost-Optimized and Secure Agent Labour Exchange via Skill-based Competence Estimation), a novel framework designed to enable autonomous LLM agents to dynamically outsource specific subtasks to specialized, cost-effective third-party LLM agents. The framework integrates mechanisms for hybrid skill representation, dynamic skill discovery, automated task decomposition, a unified cost model comparing internal execution costs against external outsourcing prices, simplified market-based decision-making algorithms, and a standardized communication protocol between LLM agents. Comprehensive validation through 239 theoretical simulations demonstrates 41.8\% cost reduction potential, while large-scale empirical validation across 240 real LLM tasks confirms 20.3\% cost reduction with proper epsilon-greedy exploration, establishing both theoretical viability and practical effectiveness. The emergence of proposed open standards like Google's Agent2Agent (A2A) protocol further underscores the need for frameworks like COALESCE that can leverage such standards for efficient agent interaction. By facilitating a dynamic market for agent capabilities, potentially utilizing protocols like A2A for communication, COALESCE aims to significantly reduce operational costs, enhance system scalability, and foster the emergence of specialized agent economies, making complex LLM agent functionalities more accessible and economically viable.
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