This course is about AI agents that can take actions in your workplace, and it uses the IMDA Model AI Governance Framework for Agentic AI as its reference point for recommended practice. It is written for supervisors and accountable managers who need to decide what an agent is allowed to do, how it is watched, and when a person steps in. The common mistake is to treat an agent like a chat assistant and focus on what it says, because the real change is what it can do with the tools it can reach. That gap shows up in ordinary work in small ways, like an agent closing a ticket, sending an email, or updating a record, and the harm is usually practical before it is dramatic. The hard part is not understanding AI terms. The hard part is drawing a boundary that holds up when the agent is busy and people are rushed.
In a typical rollout, a team enables a handful of integrations, gives the agent a goal, and adds a note that a human can review. That looks controlled, but the control depends on whether the review is real, whether someone can stop the action, and whether the action can be undone. The course stays anchored in those choices, because they are the decisions a manager can actually make without writing code or reading law. The map below shows the full shape before you meet the parts.
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An AI agent is software that can decide on steps and then take actions using tools you give it, like a ticketing system, email, a calendar, or an internal database. A chat assistant is software that produces text for a person to read and use, even when the text is very good. The difference matters because action creates side effects outside the chat, which means you have to manage permissions, approval, and rollback, not only accuracy. When someone says the agent is safe because it explains its reasoning, that is only a visible story about what it did, not a check of what it actually changed.
As a supervisor or accountable manager, you are the person who should insist on a clear answer to who owns the agent, what tools it can use, and what it must come back to a human for. You should treat action space as what the agent can reach, and autonomy as how much it can decide on its own, because widening both at once is how teams lose track of what the agent can do. Before launch, you can demand a short capability statement that includes what the agent cannot do, plus the escalation path when it hits that boundary. During use, you should watch for the moments people stop checking because the agent sounds confident, since that is when bad actions slip through quietly.
Answering the next questions tells you where your current agent habits already fit, and where they will need to change.