Most companies that try AI agents start too big. They pick a vague goal, connect the agent to everything and hope for the best. The projects that succeed do the opposite: one well-chosen task, clear limits and a launch in stages.
1. Choose a task that is frequent, bounded and measurable
The best first task happens dozens of times a week, follows a pattern you can describe and has a result you can check. Answering order status questions, sorting incoming requests or entering supplier invoices are good candidates. Negotiating with a key account is not.
2. Write down what the agent may and may not do
Before any code, list the actions the agent can take alone, the actions that need a person’s approval and the actions it must never take. This single page prevents most bad surprises and makes the project easy to explain internally.
3. Give it only the access it needs
An agent that answers customers needs to read orders. It does not need to issue refunds. Start with read-only access and add write permissions one at a time, where they have been approved.
4. Test on real past cases
Collect fifty to a hundred real examples from the last few months, including the messy ones. Run the agent on them and compare its output with what your team actually did. You get an accuracy figure before a single customer sees the agent.
5. Launch in supervised mode, then widen its autonomy
- 01First week: the agent drafts, a person approves every action
- 02Following weeks: routine cases go through on their own, unusual ones are flagged
- 03After that: autonomy grows where accuracy is proven, the rest stays supervised
Measure what changed
Track the volume handled, the time saved, the error rate and how often a person had to step in. These numbers tell you what to automate next.
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