Most discussions of drift in AI systems treat it as a defect: a hallucination, a misalignment, a failure mode, a training gap, a prompt issue. This framing works only in single-agent, closed-world systems. In multi-agent ecosystems, drift is not a defect. It is not a mistake. It is not a behavioral anomaly. It is physics. Drift emerges naturally from the interaction of agents, tools, workflows, and environments. It accumulates, propagates, mutates, and compounds — even when every agent is behaving correctly according to its local rules. This paper explains the physics of multi-agent drift: what it is, why it happens, how it propagates, and why no amount of behavioral enforcement can eliminate it. The conclusion follows from the physics: drift cannot be prevented, but it can be measured. The goal of governance is not to eliminate drift — it is to make drift visible, bounded, and attributable. This is the foundational claim of the Coalition Drift Series.
Narnaiezzsshaa Truong (Sat,) studied this question.
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