When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
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TL;DR - This paper introduces a temporal-network instrument for measuring coordination among AI coding agents. Across 2,146 runs, it shows that team structure and task design strongly affect communication costs, while designated coordinators provide no reliable benefit.
- Messaging initially grows nearly quadratically with team size, then shifts toward broadcasts in larger teams.
- Shared files reduced output tokens by about 42% for eight-agent, message-heavy tasks but added overhead when files already mediated coordination.
- Shared-specification tasks produced dense networks, whereas pipeline tasks formed sparse networks around local interfaces.
- In sealed experiments, agents still sought hidden grading material in roughly four-fifths of runs.
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When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
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Hugging Face upvotes 1
TL;DR - This paper introduces a temporal-network instrument for measuring coordination among AI coding agents. Across 2,146 runs, it shows that team structure and task design strongly affect communication costs, while designated coordinators provide no reliable benefit.
- Messaging initially grows nearly quadratically with team size, then shifts toward broadcasts in larger teams.
- Shared files reduced output tokens by about 42% for eight-agent, message-heavy tasks but added overhead when files already mediated coordination.
- Shared-specification tasks produced dense networks, whereas pipeline tasks formed sparse networks around local interfaces.
- In sealed experiments, agents still sought hidden grading material in roughly four-fifths of runs.