AI Agents That Don't Give Up

When I visited OpenAI in 2024, one of their model developers told me something that stuck with me. The real superpower of AI isn't intelligence. It's unlimited stamina.

AI doesn't get tired, bored, or frustrated, and it doesn't take breaks. It can keep working toward a goal long after a human would have stopped.

That might sound like a small distinction, but it challenges a basic assumption behind how most businesses operate: that human time, attention, and patience are limited.

Your business is built around human limits

Every business has designed itself around how much effort a customer will put in.

• Call centers are staffed around predictable customer behavior. The most sophisticated operations know almost to the minute how long a caller will wait before hanging up.
• Websites and booking engines are designed around the number of searches a typical customer will run before buying or leaving.
• Pricing, payment, and refund policies are built on the assumption that customers won't optimize the timing of their money. Few travelers track whether a rate dropped after they booked, chase a small credit, or work out whether paying now or later is the better deal.

None of this is a flaw. It's good operations design. But it means businesses have historically benefited from a friction dividend: value captured simply because customers lack the time or patience to optimize every detail.

What happens when their AI agent does?

The customer with infinite stamina

Imagine someone asks an AI agent to find the best hotel in New York for a three-night stay.

A human traveler might compare a handful of properties, check a few room rates, read some reviews, and book.

An AI agent behaves fundamentally differently:

• It can evaluate hundreds of properties simultaneously.
• It compares room categories, reads the fine print on cancellation policies and resort fees, and calculates the exact value of loyalty benefits.
• It weighs a prepaid discount against the financial flexibility of paying later and continuously monitors for rate drops long after the reservation is made.
• It can stay on hold indefinitely, call back the moment it gets disconnected, and run hundreds of automated searches without fatigue.

The difference isn't simply that AI can do more. It's that AI dramatically reduces the human effort required to pursue a better outcome. For the customer, friction nearly disappears.

The economics are about to change

When AI agents interact with businesses at scale, the consequences go far beyond a busier phone line.

Consider what happens when a customer's AI agent interacts with a business's own AI-powered customer service agent. The customer's agent can ask hundreds of questions, request granular comparisons, and challenge responses. The business's agent has to process each request, retrieve documentation, and reason through an answer.

Every one of those exchanges consumes computing resources and tokens. And the customer's agent is optimizing purely for the customer's outcome, not for the business's cost to serve them.

That creates three major implications:

1. Higher operating costs: More searches, requests, and interactions per transaction. Systems and workflows designed around predictable human traffic patterns become significantly more expensive to run.
2. Higher AI processing costs: Even when individual model calls are inexpensive, hundreds of multi-turn AI-to-AI exchanges per customer compound rapidly at scale.
3. Lost business, often without a trace: A frustrated human might call to complain. An AI agent that hits unclear or contradictory information usually won't. It will simply move on to an alternative that is easier to evaluate.

Consider two hotels offering similar rooms at similar prices. One makes its room features, amenities, policies, and benefits clean and structured for machines. The other forces an agent to parse multiple conflicting pages, interpret ambiguous language, or repeatedly ping customer service.

Which hotel is the agent more likely to recommend?

In an AI-driven marketplace, the cost of understanding your business becomes a decisive factor in whether customers choose you.

A new way to think about efficiency

For decades, businesses have measured the cost of each individual interaction: cost per call, cost per website visit, cost per service inquiry.

When one customer's agent can generate hundreds of interactions, optimizing each isolated touchpoint is no longer the right goal. The better measure is what it costs to reach a successful outcome, what you might call the cost to settle.

That changes the operational mandate. A hotel shouldn't necessarily discourage an agent from asking questions if those questions lead to a booking. The opportunity is to help that agent reach the right decision with fewer wasted steps.

In practice, that means:

• Make information machine-readable: Ensure data, policies, and product attributes are accurate and structured so an agent can verify what it needs in a single pass instead of fifty.
• Build agent-navigable workflows: Design clear, predictable pathways that allow external agents to book, modify, or resolve issues with minimal friction.
• Establish sensible interaction rules: Set governance boundaries so repetitive polling and unproductive loops don't drive up compute costs for either side.

The goal isn't to stop AI agents from being persistent. It's to make their persistence productive.

The bottom line

Businesses spent decades optimizing around human limits. They are entering a world where customer agents operate under no such constraints.

The winners won't necessarily be those that can absorb the most interactions. They will be those that make every interaction more productive.

In an AI-driven economy, making your business easier to understand is just as critical as making it easier to find.

A question to take back to your team: If an AI agent worked on your customer's behalf for hours without stopping, where in your process would it get stuck and what would that cost you?