Multi-Agent

Originally posted on LinkedIn on September 1, 2026. Earlier this year I built a multi-agent system for investment research. Several agents, each with a role, coordinated to produce a view on a set of stocks. I removed it in July. The system was complex and expensive to run, and I could not test it properly. You can’t write an assertion against a non-deterministic graph, so I had no reliable way of knowing whether the extra complexity was doing anything useful. When I eventually measured it against a much simpler quantitative approach, the simpler approach came out ahead. ...

In Post 1, I introduced Nous Ergon — an autonomous trading system that splits intelligence across four layers: LLM agents for research judgment, ML for pattern recognition, deterministic rules for execution, and a backtester for system-wide learning. This post goes inside the Research module — the layer where LLMs are found hard at work. What a Weekend Run Looks Like Over the weekend, an AWS Lambda fires. It loads the S&P 500 and S&P 400 — roughly 900 mid-to-large-cap US stocks — along with recent price history, and then distributes them across six sector-specialized teams that run in parallel. Each team screens, analyzes, and debates their sector’s best opportunities. A CIO agent evaluates the top picks across all teams and decides which stocks enter or exit the portfolio. ...