The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management

TL;DR

This paper introduces an agentic AI system for institutional asset management where specialized agents collaboratively generate, critique, and improve investment portfolios under a governance framework similar to human policies.

Contribution

It presents a novel agentic architecture that integrates multiple specialized agents and governance principles for strategic asset allocation.

Findings

Agents produce diverse capital market assumptions.

The system constructs portfolios using over 20 methods.

Agents critique and improve each other's outputs.

Abstract

Agentic AI shifts the investor's role from analytical execution to oversight. We present an agentic strategic asset allocation pipeline in which approximately 50 specialized agents produce capital market assumptions, construct portfolios using over 20 competing methods, and critique and vote on each other's output. A researcher agent proposes new portfolio construction methods not yet represented, and a meta-agent compares past forecasts against realized returns and rewrites agent code and prompts to improve future performance. The entire pipeline is governed by the Investment Policy Statement--the same document that guides human portfolio managers can now constrain and direct autonomous agents.