Article
Companies are hiring complicated AI agents before giving them a specific job.
That is a bit like hiring a very sharp person, showing them every room in the building, and hoping they spot something useful to do. You may get activity. You may even get an impressive demo. You have not given them much chance of doing dependable work.
I understand the rush. Business owners keep hearing that they are behind, that everyone else has already adopted AI, and that waiting another month means missing the whole thing. That pressure can turn a sensible experiment into a sprawling project before anyone has agreed on the problem.
When I build an AI agent, I use a simple framework called JAR: Job, Authority, Review. The image is literal. Picture putting the agent in a glass jar and closing the lid. The point is containment. Give it enough room to be useful, with clear edges around where it can go.
Job: name the work
An agent needs a job that is specific enough to inspect.
“Help with operations” is too broad. “Sort new order emails and prepare the details for review” has a shape. So does organizing files or preparing the next step in a client exit.
The job also needs a trigger. Does it run when an email arrives? Once every morning? Every Friday afternoon? Without that detail, the agent either sits idle or runs whenever someone remembers it exists.
Write down three things:
- what starts the work
- what the agent does
- what should exist when it finishes
If those answers are fuzzy, keep the agent on the workbench a little longer. A clear job is cheaper than a clever recovery plan.
Authority: put up the fence
Authority covers both information and action.
First, decide what the agent can read. It may need company values, writing examples, product information, or selected CRM records. Give it the context required for its job, not a tour of every file the company owns.
Then decide what it can do. Can it prepare a draft? Change a record? Send a message to staff? Speak directly with a customer?
These permissions should match the risk of the work. An agent that drafts an internal summary needs different guardrails from one that can quote prices or email a client. Products and prices also change, so the agent needs a dependable live source rather than an old note it treats as current.
Plain boundaries make the system easier to trust. They also make problems easier to diagnose because you know where the agent was allowed to operate.
Review: check whether it earns its keep
Review starts with the output. Is it accurate? Does it sound like your company? Did it stay inside the job and authority you gave it?
There is a second check that matters just as much: is the agent useful?
I have built agents that looked busy for two or three weeks but mainly collected noise and made documents I never checked. Nothing dramatic broke. The system simply added more stuff to maintain, which is a fairly expensive way to decorate a folder.
Review closely at the beginning. Once the work is steady, set a regular check-in. Weekly or every couple of weeks may be enough, depending on the job and its risk. Look for drift, stale information, and work that no longer helps the business.
An agent should keep earning its place. If it does not, adjust the job or shut it down.
Start with three lines
Before your next AI-agent experiment, write this on one page:
- Job: What exact work will it own, and what triggers that work?
- Authority: What can it read, change, and communicate, and to whom?
- Review: Who checks its work, how often, and what would make you stop?
That small container will not make the demo flashier. It will give the agent a much better chance of becoming steady, useful help inside the business.