Agentic Asset Allocation and the Illusion of Evaluation: Why Multi-Agent Systems Do Not Solve the Objective Problem

This article examines multi-agent architectures in institutional asset allocation—exemplified by ‘self-driving portfolio’ systems—through the lens of evaluative structure. While these systems distribute forecasting and allocation across specialised agents, selection remains governed by a fixed Investment Policy Statement (IPS). The central claim is that such architectures expand search over the portfolio space without addressing the specification or revision of the objective function. The analysis formalises a distinction between optimisation and evaluation. In current systems, the evaluative structure remains external, so errors and disagreement are processed relative to a fixed objective rather than as evidence of its mis-specification. Recasting multi-agent systems as ensemble optimisers, the article shows that model diversity does not imply outcome independence under shared evaluative criteria. A stylised simulation isolates the consequences of shared evaluation. Increasing similarity produces greater portfolio overlap, higher concentration, and stronger drawdown correlation under stress, with clustered adaptation dynamics. The article defines the objective problem, distinguishes parametric adaptation from evaluative revision, introduces evaluative crowding, and proposes Objective-Layer AI as a minimal extension in which objectives become operational variables within the system. The frontier lies in designing and governing the objective layer itself.