Treat an AI agent like a new hire before you let it loose

Before a business puts an AI agent to work, it should handle the move the way it would hiring an employee. That is the argument in an Entrepreneur contributor piece from a leader at Vida, a company that calls its agents "digital employees." The author says too many companies launch them with less care than they would give an intern.

Where things stand

AI use is common. Stanford's 2026 AI Index reports that 88% of surveyed organizations used AI in at least one business function in 2025. Agent use, meaning AI that carries out tasks on its own, stayed in the single digits across nearly every business function. Even so, companies are already using agents to answer phones, qualify leads, book appointments, follow up on open tickets and run outbound campaigns.

Define the job first

The author says vague orders such as "handle inbound calls" make it impossible to tell whether an agent works. The fix is to name the outcome you are paying for, not the activity. "Qualify inbound leads and book meetings for the sales team" is an outcome. "Handle calls" is an activity.

Then add a number and a time frame. Set the current baseline, the target and the cost of reaching it. Also decide when you will expand, redesign or stop the agent's work.

Set an owner and limits

Every agent needs a named business owner. That person answers for its results, permissions, quality standards, incidents and eventual expansion or retirement. Before launch, decide what the agent can do alone, what needs approval and what must go straight to a person.

• A customer asks for a human
• An identity check fails
• A policy exception or sensitive transaction comes up
• The agent has low confidence or repeats an error

Build a kill switch and a continuity plan before you need them. A handoff to a person should include the transcript, customer data, actions already tried and the reason for escalation, so the customer does not start over.

Keep watching the work

A dashboard of call counts is not enough. You should be able to review each interaction: what the agent received, what it said, which tools it used, which checks applied, what it did and why it handed off. Track the same measures you would for a human team, such as resolution rate, conversion rate, handle time and customer satisfaction.

The author points to the National Institute of Standards and Technology's Generative AI Risk Management Profile, which recommends continually monitoring human-AI outcomes and keeping documented processes for incidents, errors and near misses.

What to do

The author admits this is more work upfront. The payoff, he says, is a system that can cover more hours, run standardized workflows consistently and improve under human supervision. The piece is an opinion from a company that sells these agents, so read it as advice, not as a study.