How Multi-Agent AI Systems Spread Hallucinations and How to Stop Them

Multi-agent AI systems (MAS), where specialized agents reason, retrieve, and act in sequence, are increasingly deployed at production scale but carry a distinct risk called Hallucination Drift. Unlike single-agent errors, a hallucination in one agent can propagate downstream, with each subsequent agent building on false information until the final output appears confident yet entirely incorrect. A financial analysis example illustrates how a fabricated URL can cascade through parsing, analysis, and reporting stages without triggering any obvious failure signal. To counter this, engineers recommend architectural safeguards including Skeptic Nodes that verify upstream outputs, Deterministic Fences that enforce strict schemas and least-privilege permissions before any sensitive operation is executed, and Centralized State Stores that give agents reliable shared context instead of depending on error-prone in-window memory. These system-level design patterns go beyond prompt engineering and aim to make MAS deployments more robust and trustworthy in real-world applications.