This module takes you from evaluating agent outputs to governing the conditions under which those outputs are safe to produce and act on. You will learn to recognize the AI risks — hallucination, bias, data exposure, and policy violations — that most commonly surface in production agent deployments, and to use a risk-based framework to decide which controls are proportionate to your context. In the second lesson, you will apply data classification, access controls, and privacy-by-design thinking to agents that touch sensitive customer, employee, or financial information. By the end of this module, you will be able to assess risk in a real deployment, implement targeted mitigations, and produce the documentation your organization's legal, compliance, and security stakeholders increasingly require before approving broader rollout.

This module addresses two practical demands that follow every AI agent deployment: proving that the agent is delivering real value, and keeping it under control as your organization grows. You will learn how to capture baseline metrics before automation begins, measure what actually changes after deployment, and present those findings in a form that resonates with managers and finance teams. You will also build the documentation habits that prevent agents from becoming forgotten, unowned, or duplicated across your organization. By the end of this module, you will be able to quantify the business impact of an AI agent and manage its lifecycle with clear ownership, structured reviews, and principled criteria for when to evolve or retire it.