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Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (minus a processing fee of $50). 

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Mitchell Bosley, Ph.D.

From One-Off Prompts to Trained Research Agents

This seminar teaches you how to build an agentic operating system for research and professional work. It focuses on the practical problem of turning AI agents from occasional assistants into trained collaborators who can work with project materials, follow local standards, preserve decisions, and help carry larger bodies of work forward.

We will work through four core capabilities:

1. Setting up agent-readable workspaces for active projects.
2. Delegating bounded research tasks to file-aware agents such as Codex or Claude Code.
3. Reviewing outputs while preserving source discipline, decisions, and corrections.
4. Turning repeated work into reusable workflows, templates, skills, and automations.

By the end of the seminar, you will have the foundations of a personal or research-group agentic OS: a practical system for organizing project context, handing off work, reviewing outputs, and making future tasks easier because prior work has been preserved.

As a foundation for the course, you will receive an accessible introduction to what makes file-aware agents different from ordinary chatbot use. We will cover how agents such as Codex and Claude Code can work inside folders, read and revise files, run checks, update logs, and leave behind inspectable traces of what they did.

We will then turn to the design of an agent-readable workspace. You will learn how to organize source materials, project notes, outputs, logs, examples, corrections, standards, and review records so that an agent can understand the project and work with less repeated explanation.

A significant amount of seminar time will be devoted to the working loop at the center of the course:

do work → log → learn → automate

You will do real work with an agent, log what happened, identify useful patterns, and turn those patterns into repeatable instructions, templates, workflows, skills, or automations.

The final part of the course covers delegation and scaling: breaking larger research or professional tasks into bounded agent assignments, setting review checkpoints, preserving human judgment, and building a first playbook for working with AI agents across projects.