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Governing AI Agents: Visibility and Control
Summary
Learn how to deploy AI agents you can trust in production with built-in observability, guardrails, and action logging.
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.