Build a multi-agent forecasting system you can audit — in one day.
The most useful thing you can do with agents right now isn’t a chatbot — it’s a multi-agent research pipeline that produces a calibrated answer and shows its work. Bridgewater’s AIA Forecaster is the clearest published blueprint for that pattern. In one day you’ll stand up an open, paper-faithful replication, run it on real prediction-market questions (read-only), and learn the parts that generalize to any agentic research system: parallel search agents, a supervisor that reconciles them, honest calibration, and the production scaffolding — traces, reproducibility, evaluation — that makes the whole thing trustworthy.
You leave with a working repo you understand and can modify, not slides.
Workshop agenda
[Lecture] The AIA Forecaster blueprint
What Bridgewater built, why parallel agents + a supervisor + calibration beat a single model, and where the open implementation diverges from the paper.
[Hands-on] Setup & first forecast
Clone the repo, uv sync, and run a deterministic replay — no keys, no network. Read the output and locate where each stage stores its trace.
[Lecture] Stages 1–2: question rewriting & research agents
How the system reformulates an ambiguous question into precise sub-questions, then dispatches parallel agents to gather and structure evidence.
[Hands-on] Run the research agents
Run a local Ollama pass on real prediction-market questions, then open the dashboard to inspect each agent’s queries, findings, and stated uncertainties.
[Lecture] Stages 3–4: aggregation & the supervisor
Neyman extremize vs mean/median aggregation, and how the supervisor’s clarifying searches reconcile disagreement when agents diverge.
[Hands-on] Ablation lab
Swap aggregation methods and toggle the supervisor via config profiles. Run the same markets across profiles and compare outcomes side by side.
[Lecture] Calibration & evaluation
Platt scaling, Brier scores, and calibration curves. Why a confident-but-wrong agent is worse than a humble one — and what the curve actually tells you.
[Hands-on] Calibrate & score
Generate a calibration curve on the workshop dataset, read it honestly, and decide which profile from the ablation lab is the one you would ship.
[Lecture] Productionizing & where this generalizes
Daily runner, config snapshots, read-only connectors (Polymarket, Kalshi, Manifold, Metaculus). What changes live — and what to lift into your own agent work.
[Hands-on] End-to-end daily cycle
Wire a read-only connector and run the full pull → forecast → track loop. Leave with the exact command you would cron tomorrow.
Learn directly from Stefan
Stefan Jansen
Author, ML for Trading (Ch. 24: agents) · Founder, Applied AI
Who this workshop is for
ML/AI engineers shipping multi-agent or agentic-research systems who want a real, evaluated reference to study and modify.
Quant researchers and data scientists weighing whether agentic forecasting belongs in your stack — and what to test before it does.
Technical PMs and leads judging agent systems on traces, calibration, and ablations rather than vibes.
What's included
Live sessions
Learn directly from Stefan Jansen in a real-time, interactive format.
6 hours live, hands-on with Stefan
Real-time guidance through every stage of the AIA Forecaster pipeline — lecture, lab, debug. You build with the author of Machine Learning for Trading alongside, not by watching slides.
The full aia-forecaster repo
A working multi-agent system: CLI, dashboard, configuration profiles, read-only Polymarket / Kalshi / Manifold / Metaculus connectors, and 95+ tests. Cloneable today, shippable tomorrow.
11 chapter-companion notebooks
Each workshop block maps to one notebook from Chapter 24 of Machine Learning for Trading (3rd ed., 2026). Run the whole stack offline via deterministic replay — no keys, no network required.
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Frequently asked questions
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