Supervising an AI agent at work means staying accountable while software carries out tasks on someone’s behalf. This course takes its approach from the Infocomm Media Development Authority’s IMDA Model AI Governance Framework for Agentic AI, which recommends practical ways to assign ownership, set boundaries, and keep oversight real. Many teams treat supervision as a final approval step, because that feels like control. In practice, supervision is decided earlier, when someone gives the agent access, tools, and a definition of what counts as done. The work is hard because the agent’s actions can be fast, spread across systems, and easy to miss until after the result ships.
In a workplace setting, a team might connect an agent to email, a ticketing queue, and a shared drive, then ask it to clear routine requests. The team may also add a manager approval screen and assume that makes the setup supervised. The count that matters is different, because the agent may complete fifty actions before any human looks, and only a few of those actions will surface in a neat review page. The supervision question is not whether a person can click approve, but whether anyone can see what the agent is doing, stop it in time, and undo what it did when needed. Getting that wrong usually shows up as rework, disputes about who authorised the outcome, and gaps nobody can explain after the fact.
To see the shape of what supervision covers across the course, we can map it first.
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An AI agent is software that can decide and take actions in systems, using the access it has been given.
An assistant usually drafts, suggests, or summarises for a person to use. An agent can also do, meaning it can send, update, move, create, or execute inside work tools. This difference matters because action can be irreversible, like sending an external email, deleting a record, or committing a payment request, even when the instruction sounded routine.
Supervision is shared across roles, but it is not vague. A supervisor or accountable manager should decide what the agent is allowed to reach, what it is allowed to change, and where it must stop and ask. That work is yours because you understand the process the agent is entering, and you will be the person asked why an action was acceptable later.
A vendor or contractor can build or supply the agent, and IT can connect it to systems, but neither of those replaces ownership. The IMDA framework’s approach is to keep a named human accountable for each agent, so a workflow never has an agent that nobody owns. When that ownership is missing, teams fall back to screenshots, chat logs, and guesswork, because nobody can point to the authorised boundaries.
Before going further, it helps to check what you already do when software acts for you.