How Can You Handle Unexpected Traffic Spikes?
Summary
A virtual waiting room is not a technology needed only for scheduled traffic spikes, such as ticket releases or limited-edition product launches. The real criterion for deployment and operation is not whether an event is scheduled, but whether the volume of traffic arriving at a given moment exceeds what the service can handle reliably.
Today, unexpected traffic spikes can be triggered by news coverage, viral social media exposure, marketing campaigns, restocks, price changes, and increasingly, automated requests from bots and AI agents. In addition, when requests become concentrated on specific functions such as login, inventory checks, reservations, or payments, bottlenecks can emerge in one part of the service before affecting the entire system.
Rather than trying to predict every traffic spike in advance, services need an architecture that allows users to enter normally under regular conditions and queues only the excess traffic when incoming demand exceeds system capacity. In this sense, a virtual waiting room should no longer be viewed simply as a waiting page activated for major events, but as an always-ready traffic management layer that keeps unexpected demand within the system’s processing capacity.
Do You Only Need a Virtual Waiting Room for Scheduled Traffic Spikes?
No.
The need for a virtual waiting room should not be determined by whether a major traffic event is scheduled. What matters is whether the volume of traffic arriving at a particular moment exceeds the level the service can process reliably.
Traditionally, virtual waiting rooms have been most familiar in situations such as:
Popular concert and event ticket releases
Limited-edition product launches
Flash sales and Black Friday promotions
Course registration
Reservation openings
What these situations have in common is that the timing of the traffic spike is relatively predictable.
For example, if concert tickets go on sale at 8 p.m., the operations team can anticipate a sharp increase in traffic around that time and prepare the virtual waiting room in advance.
As a result, virtual waiting rooms have naturally come to be seen as queueing systems used on days when major events take place.
However, the nature of internet traffic is changing. Bot traffic now accounts for a significant share of online activity, while automated traffic generated by AI agents is also increasing. At the same time, marketing campaigns, social media exposure, and other external factors can drive far more traffic than expected.
In other words, even when there is no known traffic-spike event on the calendar, a traffic spike itself can still happen at any time.
Why Can Traffic Spike Even When No Event Is Scheduled?
Because online traffic is influenced not only by a service’s internal schedule, but also by many external variables.
Some common examples include:
Cause of Traffic Increase | Example | Predictability |
|---|---|---|
Scheduled events | Ticket releases, flash sales | High |
News and media exposure | A service is featured in the news or on television | Low |
Social media virality | A post or product suddenly goes viral | Low |
External campaigns | Ads or partner channels generate more responses than expected | Medium |
Inventory or price changes | Popular products are restocked or discount conditions change | Medium |
Bots and automation | Repeated lookups and automated requests increase | Low |
AI agents | Agents automatically act when certain conditions are detected | Still variable |
Public-sector services can also experience sudden and difficult-to-predict traffic spikes due to media coverage, press conferences, or seasonal deadlines such as tax filing, scholarship applications, and year-end tax settlement.
The important point is that regardless of what causes a traffic spike, the service still has to process the resulting demand.
How Could AI Agents Affect Traffic Spikes?
AI agents can become another source of online demand that is difficult to predict in advance.
People usually discover a product, read the page, compare conditions, and then decide whether to make a purchase. Natural pauses occur between each of these actions.
AI agents, depending on how they are designed, can automatically check user-defined conditions and immediately take the next action.
For example, a user might ask an AI agent to:
Let me know when this flight drops below KRW 200,000.
Check the conditions when this product is back in stock.
Make a reservation when the seat I want becomes available.
Find hotels that meet my target price.
This does not mean that AI agents always generate more traffic than humans, nor do all AI agents behave in the same way. Actual request volumes and patterns can vary significantly depending on the agent design, service architecture, and API policies.
What is different, however, is that multiple automated actors can detect similar conditions and respond quickly at around the same time.
This introduces a new variable that differs from traditional human-driven traffic.
Industry analysis has also pointed to the increasing complexity and unpredictability of online traffic as agentic AI activity grows. Akamai, for example, has reported rapid growth in AI bot traffic across its network, including active traffic within transactional environments such as commerce.
The key operational issue is therefore not whether AI traffic itself is good or bad. It is that incoming demand can exceed system capacity at unexpected moments.
How Can You Handle Unexpected Traffic Spikes?
As AI agents and automated traffic increase, the goal should not be to predict every incoming request in advance. Instead, services need a structure that controls inflow so that unexpected traffic does not exceed what the system can process.
This is where a virtual waiting room comes in.
A virtual waiting room converts a large burst of simultaneous requests into a manageable flow aligned with actual system capacity. Based on the configured admission limit, users are either allowed to enter the service immediately or placed in a queue. As processing capacity becomes available, waiting users are admitted sequentially.
There is one important point here:
When traffic is within capacity, users do not need to wait.
For example, suppose the system is configured to allow 100 concurrent entries and only 50 users are currently using the service. New users can continue to enter normally.
The waiting room only applies when demand exceeds the configured capacity.
In other words, there is no need to think of a virtual waiting room only as something that must be:
Turned ON at 8 p.m. tonight
for a specific event.
The basic flow is as follows.
Normal conditions
User request
→ Within processing capacity
→ Immediate service access
Traffic spike
Increase in user requests
→ Processing capacity exceeded
→ Excess requests queued
→ Sequential admission at a manageable rate
With this structure, even unexpected demand can be managed based on the system’s actual processing capacity.
Where Could AI Agent Traffic Create Bottlenecks First?
AI agents accessing a service do not necessarily create the same level of load across every page.
In practice, bottlenecks may appear first in functions where requests are concentrated, such as login, inventory lookup, reservation confirmation, or payment.
For an e-commerce service, for example, product pages may continue to load normally while the following areas begin to experience pressure first:
Login
Coupon issuance
Shopping cart
Payment
Inventory updates
From an operational perspective, the first question should therefore not be:
“Where should we display the waiting room?”
It should be closer to:
“When automated requests such as AI agent traffic surge, which part of the service reaches its processing limit first?”
In the AI Era, Is Predicting Traffic Enough?
Going forward, services need to prepare for both expected traffic spikes and unexpected ones.
Traditional traffic operations often followed a relatively predictable process:
Check the event schedule
→ Estimate expected traffic
→ Prepare infrastructure
→ Handle the traffic spike
This approach will remain important.
However, as the number of actors accessing the web expands to include bots and AI agents, and as demand can shift in real time through news coverage and social media, accurately predicting every traffic spike in advance is becoming increasingly difficult.
Traffic operations therefore need to consider two scenarios.
Scheduled Traffic Spikes
For situations with a known start time, such as ticket releases or promotions, the waiting room can be prepared in advance and admission capacity can be configured accordingly.
Unexpected Traffic Spikes
Under normal conditions, the service operates without interruption. When actual traffic exceeds what the system can safely handle, incoming demand is controlled.
Ultimately, the criterion for determining when a virtual waiting room is needed is expanding from “event schedules” to “actual system processing capacity.”
From an Event-Specific Waiting Page to Always-Ready Traffic Management Infrastructure
We previously discussed how virtual waiting rooms are evolving beyond short-term traffic-spike mitigation tools into operational infrastructure that helps maintain the stability of digital services.
The same principle applies as AI agents and automated traffic continue to grow.
What matters is not simply how many requests an AI system makes. Operations teams need to understand:
How quickly traffic is entering the service
Where bottlenecks are occurring
How much traffic the system can process concurrently
How excess demand should be managed
Ultimately, the question is not “Do we have a major event today?” but “Can the service reliably handle the traffic arriving right now?”
Unexpected demand can emerge even when no major traffic event is scheduled.
And rather than trying to predict exactly when traffic will surge, what matters more is having a structure that can control incoming traffic when demand spikes, so the service remains available and stable.