AI agents are scaling faster than the management around them
Businesses have moved quickly from asking whether to try AI agents to weighing how many to deploy and what they’ll cost. Yet broad AI use doesn’t mean autonomous agents are mature: Stanford’s 2026 AI Index found that 88% of surveyed organizations used AI in at least one business function in 2025, while agent deployment was still in the single digits in nearly every function. Companies are already using agents to answer calls, screen leads, schedule appointments, pursue unresolved tickets and run outbound campaigns. The gap between interest and deployment maturity deserves attention.
At Vida, we call these systems “digital employees.” The phrase isn’t a claim that software is human; it’s a useful reminder that putting an agent to work requires decisions about its job, authority and supervision. Too many businesses apply less care than they would when bringing on an intern.
A human hire gets a defined role, expectations, access permissions, performance measures and a manager. Agents often get vague instructions such as “manage our leads,” then operate across tools without clear oversight or measurable objectives. I’ve seen the difference across the industry: teams that plan these details before launch are better positioned to judge whether an agent is delivering value. Start with three questions.
What result should the agent deliver?
“Handle inbound calls” describes activity; it doesn’t say whether the work succeeded. A more useful mandate is to qualify incoming leads and book sales meetings. Likewise, “follow up with people” is vague, while contacting customers whose service was declined within 48 hours and scheduling appointments defines a result. The same discipline applies to a human sales or customer service hire.
Set a baseline, choose a target and a time period, and estimate the cost of reaching it. Decide in advance what performance would justify expanding the agent’s remit, redesigning its work or stopping it. Naming the outcome first makes the instructions and evaluation criteria far clearer. For more on that approach, see how to define the outcome.
Who is accountable, and when does a person step in?
Give each agent a named business owner responsible for results, permissions, quality, incidents and decisions about expansion or retirement. Then draw the authority line: what can the agent do on its own, what needs approval, and what requires escalation? Set those rules before launch. A customer asking for a person, failed identity checks, policy exceptions, sensitive transactions, low confidence or repeated errors are all reasonable escalation triggers.
Prepare a shutdown control and a continuity plan before an incident forces the issue. When a conversation moves to a human, pass along the transcript, relevant customer data, actions already taken and the reason for escalation. Otherwise, the customer may have to repeat the whole story. Autonomy without an accountable owner is unmanaged risk.
Can you inspect the work?
A count of calls or tasks won’t tell you whether an agent behaved well. You need to review interactions in context: the inputs it received, its responses, the systems and tools it accessed, the checks that applied, the actions it took and the reason for any handoff. Track business measures such as resolution and conversion rates, handle time, customer satisfaction, lead handoffs and recovered opportunities—the same kinds of KPIs you’d use to assess a human team.
The National Institute of Standards and Technology’s Generative AI Risk Management Profile also calls for ongoing monitoring of human-AI outcomes and documented ways to record and respond to incidents, errors, near misses and harmful effects. Without that record, you’re trusting a black box. With it, you can correct the agent and build its skills over time.
Manage the agent like a hire
The stronger operators treat deployment as the start of management. They define the job, establish expectations, assign an owner, put governance and escalation routes in place, review performance and keep improving the agent’s capabilities.
That takes work upfront. It can also let a business cover more hours and run standardized workflows more consistently, provided people remain involved in supervision. The real question isn’t just whether an agent can perform a task. It’s whether the business can define that task, oversee it and measure the result. If you’re considering deployment, answer those questions before scaling the headcount—or the bill.
