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This study evaluates five inter-agent communication strategies in multi-agent systems (MAS) built on large language models, revealing that no single strategy is universally optimal. The authors introduce PACT (Protocolized Action-state Communication and Transmission), which reformulates agent communication as a public state-update problem, optimizing the transmission of action-centered information. Results show that PACT significantly enhances performance-cost efficiency, achieving similar or superior task performance while reducing token usage across various MAS topologies.
PACT reduces token usage by half while maintaining task performance, revolutionizing communication efficiency in multi-agent systems.
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, effective inter-agent messages consistently preserve action-centered information needed by downstream agents. Building on this, we propose the PACT (Protocolized Action-state Communication and Transmission), which treats inter-agent communication as a public state-update problem and projects each raw agent output into a compact action-state record before it enters shared history. Across different MAS topologies, PACT consistently improves the performance-cost trade-off, achieving comparable or stronger task performance with substantially fewer tokens. The gains extend to production coding harnesses: PACT lifts OpenHands'resolve rate at -10% tokens-per-resolved, and is resolve-neutral on SWE-agent while halving input tokens. Our code is publicly available at https://github.com/iNLP-Lab/PACT.
