Overview

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This course teaches you how to assess and improve the quality, safety, and business impact of the AI agents you create. You will learn straightforward techniques for evaluating outputs, measuring reliability, and reducing hallucinations and errors. The course covers beginner friendly security, privacy, and governance practices so your agents align with organizational policies and regulations. You will design simple experiments to compare processes with and without agents, quantify time savings, and communicate results to managers. Finally, you will explore how to maintain, document, and responsibly scale your agents without creating unmanageable “agent sprawl.” By the end, you will be able to define clear output requirements, evaluate your agents systematically, and make evidence based decisions about when and how to deploy them.

Syllabus

Evaluate AI Agent Outputs

When your AI agent is handling real tickets, drafting customer replies, or summarizing account histories, "it seems fine" is not a quality standard you can defend to a stakeholder or a regulator. This module gives you the practical tools to change that. You will learn to translate vague expectations into written acceptance criteria, build small evaluation sets that anchor quality conversations with your team, and design rubrics that make manual review consistent and repeatable. You will then run structured spot-checks, use logging and tagging to surface recurring failure patterns, and apply those findings to iteratively improve your prompts and workflows. By the end of this module, you will be able to define, measure, and systematically improve the output quality of an AI agent in your own work context.

Safety, Security, and Governance for Beginners

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.

Measure Impact and Manage Agent Lifecycles

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.