Decentralized Multi-Agent Coordination and Communication in Shared Codebases

Coding agents working in shared repositories may receive separate, independently-motivated requests from human developers. When this happens, their individual implementations may work separately but conflict when combined. To study this setting, we introduce ParallACT, a benchmark of orchestrator-free multi-agent coding tasks. ParallACT contains both complementary and contradictory tasks. In complementary tasks, each agent’s task is separately useful and implementations must share an interface to be productively combined. In contradictory pairs, both agents’ tasks require editing the same code in incompatible ways, and naively combining independent solutions fails to produce a useful implementation. We compare a single-agent oracle given the combined set of tasks against multi-agent settings that vary execution order, visibility, awareness of other agents, and access to a communication channel. We find that the oracle solves most tasks while parallel agents perform substantially worse. Their performance drop is notable for complementary tasks, but sharp (e.g., 62% → 16%) for contradictory tasks where merged code typically satisfies only one specification. Simply telling an agent that a peer exists does not help significantly, and giving agents access to a communication channel only recovers part of the performance when it enforces synchronization, which comes at an additional token cost. ParallACT thus identifies and isolates a core challenge that will become more common as multi-agent coding by independent principals becomes standard, and provides a controlled test of which coordination strategies help, and at what cost.