# COALESCE: Economic and Security Dynamics of Skill-Based Task Outsourcing Among Team of Autonomous LLM Agents

arXiv: https://arxiv.org/abs/2506.01900 · HTML: https://mlsanity.com/paper/2506.01900/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation](https://mlsanity.com/paper/2402.10178/) (2024)
- [Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies](https://mlsanity.com/paper/2504.08623/) (2025)
- [TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models](https://mlsanity.com/paper/2403.17246/) (2024)
- [A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control](https://mlsanity.com/paper/2505.19301/) (2025)
- [Agent Name Service (ANS): A Universal Directory for Secure AI Agent Discovery and Interoperability](https://mlsanity.com/paper/2505.10609/) (2025)
- [ALMA: Hierarchical Learning for Composite Multi-Agent Tasks](https://mlsanity.com/paper/2205.14205/) (2022)
- [Securing GenAI Multi-Agent Systems Against Tool Squatting: A Zero Trust Registry-Based Approach](https://mlsanity.com/paper/2504.19951/) (2025)

## Built on by (most cited first, 4 of 4)

- [Virtual Agent Economies](https://mlsanity.com/paper/2509.10147/) (2025)
- [Agent Capability Negotiation and Binding Protocol (ACNBP)](https://mlsanity.com/paper/2506.13590/) (2025)
- [Error-Aware Reverse Auction Mechanism for Large Language Model Routing](https://mlsanity.com/paper/2608.12719/) (2026)
- [DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems](https://mlsanity.com/paper/2609.33768/) (2026)

## Abstract (arXiv)

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.