An AI agent that drafts a reply is one thing. An agent that books freight, adjusts a purchase order, or posts an accrual is another. Once software can act, not just suggest, a business needs a named person who answers for what it did.
Most mid-sized employers have not decided who that person is. The tool gets bought, a pilot runs, and the accountability stays vague. That gap causes more trouble than any technical flaw.
Automation used to be set and forget
Older automation followed fixed rules. You wrote the rule, tested it, and left it alone until something changed. If it broke, it broke in a predictable way.
Agents are different. They choose steps, pull from several systems, and handle cases nobody scripted. That means oversight cannot be a one-time sign-off at launch. It has to be part of someone's regular job.
Three points where a person has to be in charge
Before the agent runs. Someone defines what it may and may not do. That includes spending limits, which records it can change, which customers or suppliers it can contact, and when it must stop and ask.
While it runs. Someone watches a sample of its work, reads the exceptions, and has the authority to pause it. Without that authority, monitoring is just observation.
After it runs. Someone checks results against what the business actually needed, then adjusts the rules. An agent that closes tickets quickly but annoys your best accounts is not succeeding.
Where the stakes call for tighter control
Not every task needs a person to approve each step. Match the level of review to the cost of a mistake.
| Work type | Example | Review level |
|---|---|---|
| Low risk, easy to reverse | Internal meeting summaries, data cleanup | Spot checks |
| Medium risk | Customer emails, quote drafts, scheduling | Review before sending, or daily sampling |
| High risk | Payments, payroll, contract terms, safety or quality records | Approval required for each action |
Finance, contract management and manufacturing quality sit mostly in the third row. A wrong journal entry, a missed clause, or a falsified inspection record creates audit, legal and safety exposure that no vendor will carry for you. In both Canada and the US, regulators and auditors expect a company to show who was responsible for a decision, whether a person or a program made it.
The NIST AI Risk Management Framework is a sound public reference for setting this up. It treats governance and human accountability as core parts of managing AI, not extras.
The skills this work actually needs
Oversight fails when the reviewer cannot tell good output from bad. A plausible-looking answer in an area you do not understand is easy to approve by mistake.
That shapes who you put in the role:
- Operations: a process owner who knows how the work should flow and what an exception looks like, paired with someone who can read the agent's logs.
- Finance: a controller or senior accountant who can test the agent's entries against the ledger and who understands your internal controls.
- Sales: a sales operations lead who protects the customer relationship, checks pricing and messaging, and knows when a human call is needed.
- Manufacturing: a quality or production supervisor who can overrule the system on the floor.
- AI and data: an engineer or analyst who can trace why the agent did what it did, and fix the cause.
- Contract: short-term specialists to stand up monitoring and documentation while you decide what the permanent role looks like.
The common thread is domain knowledge first, tool fluency second. It is usually quicker to teach a strong operator how to supervise an agent than to teach an AI specialist how your business works.
Questions to settle before you scale
- Who can pause the agent, and do they know they can?
- What is the written limit on what it can do without approval?
- How will you find out when it gets something wrong, and how fast?
- Is the reviewer's time actually in their workload, or added on top of a full day?
- Who signs off when the rules change?
The last two are where most plans fall apart. Oversight added to a job that is already full gets skipped within a month.
What to do next
Pick one agent or automation you already run, or plan to. Name the owner, write down its limits, and decide how much of its work gets reviewed. Then check whether the person you named has the skills and the hours. If not, that is a hiring decision, and it is better made now than after the first costly error.
A Traverse recruiter can help you define the role and find people with the right mix of business knowledge and technical skill.