The word agent has drifted far enough that it now needs defining every time it is used. In its useful sense, an agent is a model that has been given three things a chatbot does not have: tools it can call, permissions to use them, and a loop that lets it keep going until a goal is met or a limit is hit.
Where the loop helps
Agents perform best on tasks that are bounded, repeatable and reversible: reformatting data between systems, drafting a first version of routine correspondence, monitoring a feed and flagging exceptions, or filling a form from a document. In each case there is a clear finishing condition and a cheap way to undo a mistake.
The test before you delegate
Ask what happens if the agent is confidently wrong on its worst day. If the answer is an awkward email, delegate it. If the answer is a payment, a public statement or a deleted record, keep a human approval step in the loop.
Where it still fails
- Tasks with missing context — the agent cannot know what was decided in a meeting it never saw.
- Judgement calls with no verifiable success signal, where the model has no way to check its own answer.
- Long chains of actions, because a small error early is compounded rather than corrected.
- Anything exposed to untrusted text, which can carry instructions the model may follow.
Setting one up responsibly
- Give the narrowest permissions the task needs, and separate read access from write access.
- Require explicit approval for actions that spend money, contact customers or delete data.
- Log every tool call so a failure can be reconstructed afterwards.
- Set a hard step limit and a timeout so a stuck loop stops on its own.
- Review a sample of completed runs weekly rather than trusting an aggregate success rate.
THRYV has not benchmarked individual agent products for this article and names none. The framework above is what we apply when evaluating any tool that asks for permission to act on your behalf.
