Wed, Sep 9, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Loop Engineering: Reliable Work From Coding Agents
Stefan Jansen
Stefan Jansen is the author of Machine Learning for Trading (3rd ed.), maintainer of six open-source Python libraries and 450+ teaching notebooks, and founder of Applied AI. For the last year-plus he has built production systems with coding agents across Claude Code and Codex — the exact loop-engineering discipline this workshop teaches.
Wed, Sep 9, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Loop Engineering: Reliable Work From Coding Agents
Stefan Jansen
Stefan Jansen is the author of Machine Learning for Trading (3rd ed.), maintainer of six open-source Python libraries and 450+ teaching notebooks, and founder of Applied AI. For the last year-plus he has built production systems with coding agents across Claude Code and Codex — the exact loop-engineering discipline this workshop teaches.
What you'll learn
See where the harness ends and the model begins
Learn which parts of a multi-agent system should be deterministic code and which to leave to the model's judgment.
Split one task into agents that cooperate
See how a forecasting task is decomposed into roles, and how the harness routes work between them.
Tell a demo apart from a system
Leave with a concrete rule for what an agent may do unattended and what still requires a person to approve.
Why this topic matters
A multi-agent system is mostly not prompts. It's the harness around the model: the code that splits a task into roles, routes work between agents, holds state, and checks results before they're trusted. That harness separates a demo that works once from a system that works every time. This lesson walks through the engineering behind a multi-agent forecasting system, built live in the workshop.
You'll learn from
Stefan Jansen
Author, ML for Trading · Founder, Applied AI · Investing since 2013
Stefan is the author of ML for Trading — the book and open-source companion code (19,000+ GitHub stars) that have become a practitioner reference for applying ML to financial markets. The 2026 third edition expands to nine cross-asset case studies, with a foreword by Antonio Gulli, Senior Director, Google.
He maintains the Zipline fork the quant community relies on, and built the six-library stack — data to live — behind the third edition's case studies. Investment partner since 2013, he has built trading platforms and live strategies across asset classes. In 2016 he founded Applied AI, which brings production ML to investment teams and other data-rich verticals. He has taught ML to 110,000+ professionals through DataCamp and General Assembly, incl. at Bloomberg and BlackRock.