Empirical Comparison of Agent Communication Protocols for Task Orchestration

Ivan Dobrovolskyi

cs.AIMar 24, 2026

Empirical Comparison of Agent Communication Protocols for Task Orchestration

Authors: Ivan Dobrovolskyi

Organizations: Independent Researcher, Sunnyvale, CA, USA

Abstract

Context. The problem of comparative evaluation of communication protocols for task orchestration by large language model (LLM) agents is considered. The object of study is the process of interaction between LLM agents and external tools, as well as between autonomous LLM agents, during task orchestration. Objective. The goal of this work is to develop a systematic pilot benchmark comparing tool integration, multi-agent dele-gation, and hybrid architectures for standardized queries at three levels of complexity, and to quantify the advantages and disadvantages in terms of response time, context window consumption, cost, error recovery, and implementation complexity.

Agent Communication Protocols

Agent communication protocols define how AI agents discover one another, exchange messages and state, coordinate tasks, and enforce trust.

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Mar 20, 2026Boyan Liu, Gongming Zhao, Hongli XuLLM Agent Orchestration LLM Tool Use
University of Science and Technology of China, Hefei, Anhui, China · Suzhou Institute for Advanced Research, University of Science and Technology of China
The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
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Utility-Guided Agent Orchestration for Efficient LLM Tool Use

Boyan Liu, Gongming Zhao, Hongli Xu

University of Science and Technology of China, Hefei, Anhui, China · Suzhou Institute for Advanced Research, University of Science and Technology of China

LLM Agent Orchestration LLM Tool Use LLM Agent Workflow Optimization LLM Decision-Making

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Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task performance at the expense of excessive tool calls, longer trajectories, higher token consumption, and increased latency. In this paper, we study agent orchestration as an explicit decision problem rather than leaving it entirely to prompt-level behavior. We propose a utility-guided orchestration policy that selects among actions such as respond, retrieve, tool call, verify, and stop by balancing estimated gain, step cost, uncertainty, and redundancy. Our goal is not to claim universally best task performance, but to provide a controllable and analyzable policy framework for studying quality-cost trade-offs in tool-using LLM agents. Experiments across direct answering, threshold control, fixed workflows, ReAct, and several policy variants show that explicit orchestration signals substantially affect agent behavior. Additional analyses on cost definitions, workflow fairness, and redundancy control further demonstrate that lightweight utility design can provide a defensible and practical mechanism for agent control.

Abstract

Agent Communication Protocols

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