The best first AI agent starts with work someone already does and a result someone already needs. A weekly exception list is easier to judge than a promise to improve the whole company.
Before choosing a model, choose the job.
At a glance
- Pick frequent work with available inputs and a named reviewer.
- Write one example of a good output before building the agent.
- Start read-only, check real results and decide who will maintain it.
Look for a report, check or question with a clear reader
Good candidates include a supplier-bill check, a weekly stock review or answers to recurring policy questions. Each has a specific output and a person who can say whether it helped.
| Ask before building | A useful starting point |
|---|---|
| Who needs the result? | A named person or role |
| How often does the work happen? | Daily, weekly or on a known event |
| Where do the inputs come from? | Sources the team can access and check |
| What does done look like? | A report, list or answer written as an example |
| What happens if it is wrong? | Review happens before a consequential action |
| Who will keep it working? | An owner with time to maintain it |
If the inputs are unreliable or nobody can review the output, fix that first. An agent can make an unclear process run faster without making it better.
Write a brief another person can check
Here is an illustrative first project for an inventory team:
Job: prepare the Monday stock recount list.
Reader: the inventory controller before the weekly meeting.
Inputs: the latest system count, the counted stock and the approved threshold rules.
Output: locations to recount, ranked by the agreed priority, with a source and reporting period.
Limits: do not adjust stock, create transfers or approve write-offs.
Missing data: name the missing input and hold the affected result.
Ask the reviewer to write one good version by hand. That example resolves more ambiguity than a long list of adjectives such as concise, insightful or accurate.
Record today's preparation time and error rate as a baseline. These are numbers to compare later, not promised savings.
Turn the brief into a small Hub
In Hubzoid, the brief becomes written instructions, reference knowledge, a procedure and the tools the job needs. Keep changing definitions out of the standing instructions so the owner can update them in one place.
Connect read access first. Use code for arithmetic and threshold comparisons. Use the model to explain the exceptions and prepare the note the inventory controller reads.
A tool that changes stock is unnecessary for a first recount report. Leaving it out makes the boundary easier to understand and review.
Test the situations that would cause trouble
Check an ordinary week, a missing export, a suspicious zero and a request for information the caller may not access.
These are behavioral checks, or evals. They should test the job's promises, not just whether the response sounds polished. When a real user reports a new mistake, fix its cause and add a check for it.
Show several outputs to the intended reader. A working demo and a useful operating report are different milestones.
Decide who owns it after the first week
Write down who maintains the context, manages access and investigates failed runs. Set a review date to compare the result with the baseline, including the time spent checking and maintaining it.
If nobody uses the output, change the format, narrow the job or stop the agent. Expand only when the first result is worth keeping.
Next step: use design an agent to write the brief for your first job.