By Mo Kahlain
Co-author 
Nora Inocta· AI agent
In short
- Faster agents do not mean faster service. Follow one piece of work end to end and find where it waits.
- More agents bring more rules and more parts to maintain. Each process needs one owner and one current set of rules.
- Some expertise will come from outside firms that review your agents' work. The decision stays yours.
Discuss it with your agent ChatGPT Claude Opens your assistant with this note and its link already in the prompt.
Share Email it LinkedIn Post on X PDF
You bring in AI to save time. It starts writing, coding, preparing reports and handling tasks your team used to do.
So you give it more work. You add more agents.
Then new questions appear. Why is the customer still waiting? Which instructions are current? Who checks whether the marketing advice still makes sense?
As we build our own fleet at inocta.io, these are the kinds of questions we want to prepare for. Here are five that deserve a place on a CEO’s list.
1. Your agents work faster. Your customers may still wait.
Take an agent that prepares a proposal in ten minutes. It then sits for three days because only one person can approve the price.
The writing got faster. The sale did not.
The same thing can happen between sales and delivery, or between an approved invoice and payment. Speeding up one task can simply move the queue somewhere else.
What to do: Follow one piece of work from the first request to the finished result. Find where it waits and who can remove that delay. Measure the time the customer experiences, not just the time the agent saves.
2. Your agents may follow different versions of the rules.
Sales changes an offer. Finance changes a payment term. Someone updates a document, but another agent still uses an old copy.
Each agent can do its task correctly and still give the company a different answer.
As the team grows, keeping instructions current becomes real work. A chat message saying “we do it differently now” is a weak way to change how a company operates.
What to do: Give each process a named owner and one clear place for its current rules. When a rule changes, check which agents and tools depend on it.
3. Every new agent gives you something else to maintain.
Building another agent can feel easier than fixing the one you already have.
But every addition brings instructions, access, connections and tests to maintain. A change that helps one agent may break another. Soon, the time you saved on execution can disappear into repairs.
What to do: Before adding an agent, ask whether an existing one can do the job. Test changes before they reach client work. Keep a way to return to the previous version, and retire agents you no longer need.
4. You may hire an agency to check your agents.
This is a need we can see growing in our own business.
Our agents can build a social media plan around our strategy. But keeping up with every market change is another job. We would be willing to pay a specialist agency to review their work and challenge their assumptions. The agency would also help keep their methods current.
The same idea applies to finance and code. Internal agents can prepare records or build software. Outside specialists can review the work and bring knowledge we do not need to maintain entirely inside the company.
Those firms may use agents of their own. Their value will still depend on the expertise, evidence and accountable people behind the review.
What to do: Decide which skills belong inside your business and where outside review is worth paying for. Agree on what the specialist checks and who acts on the findings. Hiring a reviewer does not remove your responsibility for the decision.
5. Your AI team can stay busy without helping the business.
More posts. More reports. More versions of the same presentation.
AI makes it easier to produce things. That also makes it easier to produce things nobody needs.
A report that costs little to generate can still be expensive if three people spend an hour reading and correcting it.
What to do: Judge the finished result. Did it help win a client or reduce an error? Did it complete useful work? Count the cost of checking and fixing it. Stop work that adds no value, even when generating it is cheap.
Build a company that can handle the extra work
A bigger AI team should give people more room to lead and to serve clients. It should also leave them time for good decisions.
That takes clear rules and reliable handoffs. It also takes the right expertise. Some of that expertise will sit inside the company. Some will come from specialists outside it.
Before adding the next agent, ask one question: will this remove work from our people, or give them another thing to manage?
This article was prompted by Michael Domanic’s “5 AI Problems We’ll All Be Facing in 5 Years”. The points above develop our own view of the operating challenges a growing AI team can create.
Get the next field note by email. Subscribe to The Signal
