AI agents can now read alerts, query systems, compare data and draft actions. In markets operations, where every morning starts with checks on positions, prices, reports and overnight jobs, that is genuinely useful. It also raises a question that matters more than any model choice: what may an agent do on its own?
Our answer is a simple rule. The agent may watch, sort, explain and draft. A person approves, changes and releases. Here is how to apply it.
1. Begin with the checks people already do every morning
The best first use is not a new process, but an existing one. Most operations teams have a morning routine: did the overnight batches finish, did the prices arrive, are the positions reconciled, were the reports accepted. These checks are repetitive, well understood and easy to verify. That makes them the right place for an agent to start, because everyone already knows what good looks like.
2. Let the agent sort and explain, not decide
An agent is most useful when it reduces a long list to a short one. Instead of two hundred alerts, the team sees eight groups, each with a likely cause and the evidence behind it. The agent explains its reasoning in plain language, so the engineer can agree or disagree quickly.
What the agent should not do is close an alert, change a setting or rerun a job by itself. Sorting is safe. Deciding is not, until the team has seen the agent be right for long enough.
3. Keep a human approval for anything that moves money, trades or reports
Draw the line clearly and write it down. Anything that changes a trade, a payment, a position or a regulatory report needs a named person to approve it. The agent can prepare the change, show what it will do and what could go wrong, and wait.
This is also where regulation is heading. The EU AI Act requires human oversight for high-risk AI systems, and supervisors expect firms to explain how automated decisions are controlled. A clear approval step answers both.
4. Log every action so it can be audited
Record what the agent saw, what it suggested, who approved it and what happened next. This is not only for auditors. It is how the team learns where the agent is reliable and where it is not, and how you widen its role over time with evidence rather than hope.
Keep the data where it belongs. Agents should work inside the client's environment, and client data should never be used to train models.
5. Measure what it catches, not what it automates
The wrong measure is the number of tasks automated. The right measures are the issues found earlier, the time from alert to fix, and the errors that did not reach a report or a client. If those improve, the agent is earning its place. If they do not, more automation will not help.
The result
Used this way, AI does not replace the operations team. It gives them their mornings back, and it leaves every decision that matters with a person who can be asked about it.