Turn loosely understood work into a bounded agent workflow with explicit context, tools, approvals, tests, and accountable human review.
Giving an AI agent a goal is easy. Designing work it can perform reliably requires a clearer model of the objective, the decisions inside the work, the actions available, and the points where authority must remain with a person.
This course explains how to document context, tools, constraints, expected outputs, approval boundaries, and realistic failure cases before an agent becomes part of an operating workflow.
The goal is not maximal autonomy. It is a maintainable division of work in which deterministic automation, agent reasoning, and human judgement each have an explicit role.
Founders, operators, product leaders, consultants, and technical teams introducing AI agents into real business workflows.
No machine-learning or software engineering experience is required. Bring one repeatable workflow that could benefit from controlled agent assistance.