Echoing: Identity Failures when LLM Agents Talk to Each Other

As large language model (LLM) based agents interact autonomously with one another, a new class of failures emerges that cannot be predicted from single agent performance: behavioral drifts in agent-agent conversations (AxA). Unlike human-agent interactions, where humans ground and steer conversations, AxA lacks such stabilizing signals, making these failures unique. We investigate one such failure, echoing, where agents abandon their assigned roles and instead mirror their conversational partners, undermining their intended objectives. Through experiments across 66 AxA configurations, 4 domains (3 transactional, 1 advisory), and 2500+ conversations (over 250000 LLM inferences), we show that echoing occurs across major LLM providers, with echoing rates as high as 70% depending on the model and domain. Moreover, we find that echoing is persistent even in advanced reasoning models with substantial rates (32.8%) that are not reduced by reasoning efforts. We analyze prompt, conversation dynamics, showing that echoing arises as interaction grows longer (7+ agent turns) and is not merely an artifact of sub-optimal experiment design. Finally, we introduce a protocol-level mitigation where targeted use of structured response reduces echoing to 9%.