System-Design

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. ...

Originally posted on LinkedIn on August 4, 2026. In my last post I introduced Nous Ergon, an applied AI lab. Since then, while building inside my multi-platform agentic workflows, I have been running head-first into a notable pain point: how to keep a fleet of agents doing things the same way? Every agent does things slightly differently. Same task, different direction, different quality. As a solo operator supervising this fleet, I have a strong interest in making sure the agents are delivering a standardized, conforming set of deliverables — so I wrote my expectations down. That corpus is now 35 policy documents and well over 100,000 tokens. ...

The Thesis Can AI generate sustained market alpha — not through a single model making predictions, but through a system of specialized components, each contributing what it does best? That’s the question behind Nous Ergon: Alpha Engine (νοῦς ἔργον — “intelligence at work”), a fully autonomous trading system I’ve been building that combines AI-driven research, quantitative prediction, and rule-based execution. Quantitative finance — using mathematical models and statistical analysis to make investment decisions — has traditionally been the domain of institutional hedge funds with massive engineering teams. Large language models and modern machine learning tooling are changing that equation. ...