Every product team experimenting with AI agents eventually hits the same question, usually after something goes wrong: who decided the agent was allowed to do that? The technology conversation moves fast. The ownership conversation is where teams actually get into trouble.
An agent is a feature with judgement built in
Giving software autonomy multiplies the cost of a wrong decision. Before a team ships an agent, someone needs to own what “good” looks like, what it's allowed to do unsupervised, and what happens when it's wrong.
That ownership question rarely has a clean technical answer. It's product work: deciding where autonomy earns trust and where a human still needs the wheel.
Scope the agent's job, not its intelligence
The teams getting real value aren't chasing the smartest model. They're narrowing the agent's job until its failure modes are boring and its successes are obvious. Start there before expanding what it's trusted to do.
What this looks like in practice
Take a support inbox that hands routine refunds to an agent. Refunds under $50, for a known return reason: the agent decides and acts immediately, no human in the loop. Refunds involving a policy exception, a repeat complaint, or a larger amount: the agent drafts a recommendation and a person approves it before anything happens. That dollar threshold isn't a technical limit. The model could handle any amount. It's a deliberate choice about where the business is comfortable letting judgement run on autopilot.
In plain terms
An AI agent is software that can take real actions on its own (send an email, issue a refund, update a record) rather than just suggesting what a person should do. Giving it that power is like hiring someone and deciding, before their first day, exactly what they're trusted to handle alone and what still needs a manager's sign-off. Skip that step, and you find out where the line should have been only after something's already gone wrong.