Ex-BlackRock Exec Ang Details 50-Agent Investment Process
Ang and Altbridge researchers lay out an architecture for autonomous portfolio management
Financial firms have been moving away from the one-size-fits-all approach when it comes to AI.
Companies are using smaller, specialized systems because they’re easier to test, control, and generally, are more consistent.
Some examples:
Capital One replaced a single LLM with a multi-agent system for call summaries, where agents interpret, reason, cross-check, and document the interaction before finalizing it.
Daloopa built dozens of narrow models, each trained on structured financial data and focused on one task. “Our models have an IQ of 250 on one task and 2 on something else,” Thomas Li, CEO and co-founder of Daloopa, told me in September.
At BlackRock, researchers broke stock screening into specialized agents—fundamentals, sentiment, and valuation—that debate and cross-check each other before reaching a final decision.
A new paper from Andrew Ang, a former BlackRock executive, Nazym Azimbayev, a sovereign wealth fund CIO, and Andrey Kim PhD, a Deutsche Bank quant, takes the BlackRock debate architecture further.
The paper, the Self-Driving Portfolio: Agentic Architecture for Institutional Asset Management, asks if autonomous driving is here, why not autonomous investing?
Their answer is a 50-agent pipeline that runs the process and documents each step of its reasoning.
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They view running a strategic asset allocation process at an institutional level as a bandwidth problem as much as an analytical one. A CIO can supervise maybe 10 to 15 investment departments. A research team can realistically cover 20 to 30 asset classes before the process bottlenecks and an investment committee meets quarterly.
To speed this process up, they built a 50 agent pipeline that produced a documented strategic asset allocation with capital market assumptions, portfolio construction, peer review, and a board memo.
Here’s what they built
The pipeline is organized around the Investment Policy Statement (IPS), which governs the whole system the way it would govern human portfolio managers. Every agent reads it; the chief risk officer agent checks compliance for every portfolio candidate; the final output must satisfy it.
To illustrate how the pipeline works in practice, the authors ran it in March 2026 against the following mandate: 18 liquid asset classes (6 equity, 8 fixed income, 4 alternatives), a target real return of CPI +3–4%, a volatility band of 8–12%, a maximum drawdown of −25%, and a tracking error ceiling of 6% relative to a 60/40 benchmark.
Macro agent: Classifies the current economic regime — expansion, late-cycle, recession, or recovery — using macro data, market indicators, and web searches for real-time readings. Output flows downstream to every other agent.
Asset class agents: Agents run in parallel, one per asset class. For equity classes, each estimates expected returns using six different methods, then blends them into a seventh composite. An LLM-as-judge step reads all seven alongside the current macro regime and valuations, and selects a final estimate with explicit weights and a written rationale.
Portfolio construction agents: 20 agents each build a portfolio using a different method, ranging from simple rules of thumb to more sophisticated approaches. A 21st researcher agent scans the academic literature and proposes methods not yet in the pipeline. A separate adversarial diversifier, one of the original 20, deliberately constructs the portfolio most different from the consensus of all the others.
Strategy review: Each agent reviews two others — one using a similar approach, one using a different one — and all reviews are released simultaneously. Agents then vote, ranking their top five and flagging a bottom pick. Votes are combined with a performance score, and the final shortlist must include methods from at least three of the four broad categories.
CIO agent: Combines the top candidates using seven different aggregation methods and selects the one best suited to the current environment. Produces a board memo written for non-technical stakeholders.
Meta-agent: After each rebalancing cycle, compares past forecasts against realized returns, identifies systematic weaknesses, and updates both the code and instructions governing the other agents. All changes are logged.
Here’s what they found
The macro agent classified the current environment as late-cycle with stagflationary risk.
When each asset class agent settled on its return forecast, the pattern was consistent: the more expensive the market, the more the agent discounted historical estimates. US Growth stocks had their forecast cut 2.0 percentage points below the composite; US Large Cap was cut 1.1 points; Emerging Markets were barely adjusted. The agents weren’t pessimistic across the board — they were specifically skeptical of backward-looking estimates for the assets where current prices already implied low future returns.
The same reasoning surfaced in the portfolio construction vote. In a late-cycle environment where return forecasts are uncertain, the agents collectively favored methods that lean on historical volatility and correlation data rather than return predictions. Maximum Diversification — a method that spreads risk across assets without relying heavily on return forecasts — ranked first. The portfolio that was deliberately constructed to be as different as possible from all the others came last, which was expected: its value is in the final blending step, not as a standalone recommendation.
The final portfolio came out modestly underweight stocks (44.9% vs. 60% in a standard balanced portfolio), roughly in line on bonds (41.7%), with an 8.1% cash position. Over a backtest from 1996 to 2026, it produced nearly the same return profile as a 60/40 portfolio — but with a peak-to-trough loss of 25.6% versus 34.3%.
To be sure, this is a proof of concept, not an investment strategy. One run producing a sensible-looking portfolio doesn't tell you much given the short-time horizon.
I asked the paper’s authors about the results and received email responses from Azimbayev, who is also CEO of Altbridge, which describes itself as an AI-native hedge fund.
