Lost Between Agents: How Do Task Instructions Drift Across LLM Agents?

Multi-agent LLM systems solve complex tasks through communication, and successful collaboration depends on whether important task information is preserved as messages pass between agents. Existing evaluations mainly measure final task performance or overall message similarity, but they do not show what changed during communication or whether those changes actually mattered. This gap raises a key question: how can we identify information loss in agent communication and determine which changes affect downstream behavior? Importantly, a change that occurs frequently is not necessarily the one that matters most to the receiving agent. To address this question, we study sequential task specification handoffs and introduce CommAudit, a protocol for auditing how specifications change as they pass between agents. CommAudit measures both how often task constraints change, which we call exposure, and how much those changes affect the executor, which we call impact, using matched repair and control interventions. Using this protocol, we find that repairing the changes identified by CommAudit consistently improves executor accuracy over stylistic controls across communication agents powered by four different LLMs. Moreover, across three benchmarks and four communication agents, the most frequent change type differs from the one with the largest downstream effect in six of twelve settings, while the same change counts can still lead to different repair outcomes.