arXiv:2606.05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost. We analyze five common inter-agent communication strategies across two MAS topologies, finding that no fixed strategy is universally optimal. Instead, effe
The proliferation of multi-agent systems built on large language models is exposing the inefficiencies of unconstrained natural language communication, necessitating more structured approaches.
Optimizing inter-agent communication directly impacts performance, cost, and scalability of multi-agent AI systems, which are foundational to future AI applications.
The focus in multi-agent system design shifts towards more constrained and efficient communication strategies, moving beyond simple free-form natural language exchanges.
- · AI software developers
- · Cloud providers (cost reduction)
- · Enterprises adopting AI agents
- · Inefficient multi-agent system architectures
- · LLM providers (potential for fewer tokens per task)
More robust and cost-effective multi-agent systems become viable, expanding their deployment across various sectors.
New MLOps tooling emerges to standardize and manage inter-agent communication protocols and strategies.
Increased complexity in agent design, pushing towards specialized communication models alongside general-purpose LLMs.
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Read at arXiv cs.AI