Demonstrates how drift arises from multi-agent dynamics, suggesting implications for AI governance.
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.
No takes yet. Share an insight, caveat, or question.
Narnaiezzsshaa Truong (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: