When a slot opens, let the AI agent act – within limits

When a slot becomes available, allowing an AI agent to act within certain limits can be highly beneficial. Instead of focusing on what AI agents can replace, it's more useful to consider what repetitive tasks they can remove while leaving important decisions to humans.

Consider a simple appointment scenario. A customer needs a consultation next week, with Tuesday or Thursday afternoon between 02.00pm and 05.00pm being preferable. The booking page doesn't show any suitable slots. The customer checks later, only to find no available slots. They might call the service provider, and an administrative staff member would check the same calendar, giving the same answer.

If another customer cancels a Thursday 03.30pm appointment, the right slot suddenly becomes available. However, unless the waiting customer happens to check at that exact moment, the slot might disappear again.

This isn't a spectacular AI problem, but it highlights a useful application. The traditional workflow involves people repeatedly asking "has anything changed?" Instead, a more useful system would allow the customer to state their requirements once: preferred days/times, location, and whether the system can notify them or book the appointment. An AI model can interpret these preferences and turn them into structured constraints.

However, much of the work should be conventional software. An availability service watches an authorized booking API, a matching engine compares newly released slots with stored preferences, and rules check factors like time, location, service type, and eligibility. There's no need for an LLM to determine if a slot falls within a specific time frame.

The agent should not have unlimited authority. If it finds a matching slot, before making the booking, the system should verify the slot still exists, check for conflicts, apply eligibility rules, and confirm what the customer has authorized. Different levels of autonomy can be defined based on the customer's level of trust. For instance, a cautious customer may only want notification, while another may allow temporary holds and approval.

Someone else may pre-authorize bookings within defined conditions. Regardless, if a customer authorizes a specific time slot, a nearby alternative should not be accepted automatically.

Operational details matter in production environments. If two customers try to take the same newly released appointment, the system must verify the result rather than assuming success. It should keep the customer's request active and send the request again if necessary, using mechanisms like idempotency to prevent duplicate reservations.

There should also be a stopping rule for when the AI agent needs to return decision-making to a person. This could be in cases involving payment, cancellation charges, conflicting appointments, unclear eligibility, or situations where the AI's authority is exceeded. This is not a failure of automation, but rather a crucial aspect of good automation.

The objective should be useful autonomy, not maximum autonomy. The business case for AI agents goes beyond appointment booking, as similar patterns appear in various services where supply changes throughout the day, but customer preferences remain constant. By implementing an availability agent, businesses can reduce routine administrative work and improve responsiveness for customers.