Decorrelating the Advice Layer: A Calibrated Fragility Metric and Tested Mitigations for Synchronized Liquidation in Agentic Wealth Management

Abstract

Conversational AI agents are turning wealth management into a continuous loop of market analysis, allocation, risk checks and execution, run for millions of clients on a handful of foundation models. We build a multi-asset agent-based model of this layer, calibrated to measured model concentration, cross-model correlation, retail turnover and 33 years of real equity returns, and ask when the layer amplifies market declines and what prevents it. The effective number of independent decision-makers is bounded however many clients the layer serves: about 2.0 at today's consumer concentration. This monoculture of views drives crowding in individual securities and sets the blast radius of a compromised model provider, but it does not drive market-wide liquidation. Index tail losses rise with the square root of agentic adoption, are 1.47-1.90 times larger under an agentic than under a periodic robo-advice architecture, and do not rise with model concentration; synchronized automation (risk overlays, drawdown cuts and attention-driven execution) drives them. A fragility index computed from aggregate flow, public returns and an impact estimate forecasts tail liquidation out of sample (hold-out R^2 of 0.56) far better than concentration or correlation measures. Decorrelating the automation, not the models, works: dispersing overlay parameters, staging execution and a participation breaker together cut tail liquidation by 63% at no measurable cost to client welfare, whereas provider diversification has no effect. Because the statistics required are sums, they can be computed across platforms under secure aggregation with differential privacy at negligible cost for realistic client populations. In historical replays, an agentic layer holding 10% of the market would have deepened the 2020 crash by about 858 basis points.