AI agent examples are useful when they show what the system is allowed to do, what it must ask a person to approve and how someone can reconstruct its work. The same label can hide very different levels of autonomy.
1. Research brief agent
A research agent can search an approved source set, collect passages and draft a brief with links. It should not turn weak sources into facts or recommend a consequential action without evidence. Its output needs source URLs, quotes or excerpts, gaps and a person who approves the conclusion.
If the source list and sequence are already fixed, a regular research workflow may be simpler. Use an agent only when the next useful source or question depends on what it finds along the way.
2. Support triage agent
A support triage agent can classify an incoming issue, retrieve approved policy and draft a response. It may route a billing exception or safety issue to a person. It should not send a message, promise a refund or alter an account without permission.
The review question is simple: can you see why the issue was classified, which policy was used and why the case was escalated? If not, the system is hard to trust when it is wrong.
3. Operations handoff agent
An operations agent can read a completed intake, check whether required fields exist and prepare handoff tasks. It can flag missing details and draft the handoff note. A human should still approve changes to owners, deadlines, budgets or external systems.
This example often starts better as a checklist. Add agent choice only if the exceptions are frequent enough that a fixed route no longer works.
4. Content review agent
A content review agent can compare a draft with a supplied style guide, source list and claim policy. It can return unsupported claims, missing evidence and questions for the writer. It should not invent proof or publish material directly.
Its test cases should include a strong draft, a missing citation, a contradictory source and an instruction that asks it to ignore the policy. The hard cases reveal whether the guardrails are real.
5. Incident investigation agent
An investigation agent can gather logs from approved read-only tools, summarize observations and propose next checks. It should keep a record of each tool action and stop before restarting services, changing production settings or sending external updates.
This is a useful pattern when there are many possible paths to inspect. It needs the clearest permissions because a wrong operational action can be expensive.
Decide what your task needs
For each example, write down the goal, available input, permitted tools, actions requiring approval and a failure case. If you cannot name a useful reason for the system to choose its own next step, start with a fixed workflow instead. The AI agents guide explains the distinction, and the related course lets you practise it through a complete design sequence.