AI agents often look impressive in demos but struggle when they hit production, where you need visibility, safety controls, and clear accountability for every action an agent takes. In this hands-on course, Kesha Williams—a machine learning technology leader with 25+ years of experience—shows you how to work in Python to transform an ungoverned shopping agent into a governed system that behaves predictably in real-world environments.
Through hands-on coding in GitHub Codespaces, learn how to add structured logging to make agent behavior observable, implement runtime guardrails that block unsafe actions, and introduce human-in-the-loop approval workflows for high-risk changes. Kesha also demonstrates how to build an agent inventory and a reusable deployment checklist that you can adapt to your own framework, giving you a practical governance tool kit—whether you are shipping your first agent feature or hardening an enterprise AI workflow.
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