Using Agentic AI to Manage Spiky "Japa" Seasonal Travel Bookings and Visas

Imagine running a travel agency that normally handles 100 customer enquiries a day. Then September approaches.

Suddenly, students are asking about visas, flights, accommodation and travel dates. Some need help understanding a document requirement. Others want the cheapest flight to the UK. A few have already booked and need help after an airline changes their schedule.

The problem isn't necessarily a lack of customers. It is having enough people to handle all of them well.

This is where agentic AI could change how travel agencies manage seasonal demand.

Rather than using AI simply as a chatbot that answers questions, a travel agency could use an AI agent to coordinate parts of a customer's journey from finding visa information to comparing flights while leaving sensitive decisions and high-impact actions to humans.

What makes an AI Agent Different?

A chatbot generally responds to whatever a customer asks. An agent can take a broader goal and work through several steps to achieve it.

Consider a student saying:

“I've been admitted to a university in the UK. My course starts in September. I need help sorting out my visa and flight.”

A useful agent could first identify what information is missing, retrieve relevant visa guidance, create a checklist and then search for flights based on the student's preferences. It could also remind the student about outstanding tasks.

This doesn't mean giving the AI complete freedom. Anthropic's guidance on building effective agents recommends starting with the simplest architecture that solves the problem, rather than adding unnecessary complexity.

For a travel agency, that could mean one central agent connected to a small number of approved tools.

A Practical Travel-Agent Setup

The architecture could look something like this:

Customer → AI agent → approved tools → travel systems → human support

The agent handles the conversation and coordinates tasks. External systems provide information that changes in real time.

For visa questions, the agent could use a retrieval system containing approved information from official immigration sources. This is important because visa requirements can change, and a language model should not be treated as the final authority.

For example, the current UK Student visa guidance says applicants applying from outside the UK can apply up to six months before their course starts. It also outlines requirements such as having an offer from a licensed student sponsor and meeting the relevant financial and English language requirements. The agent could turn that information into a personalised checklist instead of making the student search through several pages herself.

From Visa Questions to Flight Bookings

Once the visa process is underway, the next headache is usually the flight.

A student might tell the agent:

“Find me an affordable flight from Lagos to Manchester in September. I don't want more than one stop, and I need a checked bag.”

They can translate that request into specific search criteria and send it to an authorised flight-search service.

This is where airline distribution technology becomes useful. IATA's New Distribution Capability (NDC) is a standard designed to improve communication between airlines and travel sellers and provide richer airline offers for comparison.

The AI shouldn't invent prices or availability. It should retrieve actual offers, compare them against the customer's requirements, and explain the differences.

For instance, the cheapest flight might have two stops and no checked baggage. Another option might cost slightly more but include baggage and have only one stop.

Instead of simply saying, “This is the cheapest,” the agent can explain the trade-off.

The final purchase should also require confirmation. Searching for a flight is one thing; spending a customer's money is another.

The workflow becomes:

Search → Compare → Recommend → Customer confirms → Book

That small approval step prevents an innocent misunderstanding from becoming an expensive mistake.

The system could also limit how many times an AI agent calls an external service. If a flight API stops responding, the agent shouldn't keep calling it indefinitely. It should retry within defined limits and then escalate the problem.

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