Managing AI Researchers

Can artificial intelligence (AI) agents based on a large language model (LLM) carry most of the load in strategic asset allocation? In their April 2026 paper entitled "The Self-Driving Portfolio: Agentic Architecture for Institutional Asset Management", Andrew Ang, Nazym Azimbayev and Andrey Kim present a 6-step strategic asset allocation system in which:

1. A macro agent identifies the economic regime (expansion, late-cycle, recession or recovery).
2. Asset class agents each assigned one class run in parallel to estimate respective expected returns, expected volatilities and confidence levels.
3. A covariance agent generates an asset class covariance matrix.
4. Portfolio construction agents each independently employ Step 2 and 3 outputs to proposed a portfolio based on an assigned method (such as equal weight, inverse volatility, mean-variance optimization or risk parity), including:
   • A researcher agent to propose novel portfolio construction methods.
   • An adversarial agent to uncover unconventional allocation ideas.
5. Multiple agents review all proposed portfolios and vote on them.
6. A chief investment officer agent scores, selects and combines surviving proposed portfolios using an ensemble of seven combination methods. This agent then summarizes a final recommendation/reasoning/dissenting views.

They include a meta-agent that compares forecasted and realized returns and rewrites agent scripts to improve future performance. They specify each agent in this system via a description, a set of scripts, a collection of skills and a structured output. An Investment Policy Statement (specifying asset class universe, objective, tracking error) constrains the AI agents. Overall, this system compresses days or weeks of human work into minutes. Based on prior research and experience with LLM-based AI agents, they observe that: