In this article, we'll take a closer look at AI travel agents, discover their main elements, grasp how they differ from the chatbots we are already used to, find out how they impact all the industry players, and help you build an efficient implementation plan to create your own agent.
AI and the tourism sector in numbers
AI adoption in the travel industry is only accelerating. According to Statista’s November 2024 survey, 40% of travelers globally stated they utilized an AI-based tool for travel planning. But last year’s achievements are just the beginning. After 5 years of research, researchers concluded that AI and machine learning accounted for nearly two-thirds of global tech investment deals made by travel and mobility corporations. How does it translate into the global market size?
According to GrandViewResearch, the worldwide cost of AI in the travel sector is growing at a rapid pace. Estimated at USD 3,373.0 million in 2024, it’s predicted to rise to as much as USD 13,868.8 million by 2030 - such growth is rarely seen in any other new technology these days.
What are the mechanisms that shape such a profitable synergy of AI and travel businesses? Let’s break them down.
Why does AI matter in tourism?
To find an answer to this question, we'll define specific advantages artificial intelligence brings to the travel industry, and define the place of travel agent AI solutions in this equation.
Increased efficiency. AI radically restructures the planning of travel activities - it can immediately analyze multiple huge datasets to provide recommendations to travelers based on their preferences. They also automatically check responses for compliance with company travel policies. AI agents boost this efficiency by understanding and completing orders using natural language requests and can execute even complicated multi-step bookings (with several suppliers) through simple conversation interfaces.
Lower costs. By automatically applying corporate discounts and keeping an eye on fare fluctuations, AI systems show excellent abilities to determine the best prices. A virtual travel agent goes further: it negotiates in real-time across suppliers for the best deals, bringing value both for businesses and tourists. For this reason, 90% of executives acknowledge AI's role in cutting costs, as stated by Boston Consulting Group.
Tailored recommendations. Machine learning algorithms analyze data, such as travel history, loyalty memberships, and behavioral patterns, in order to make custom recommendations that boost engagement, according to Milton. AI agents can further engage users in a natural conversation to gain info about contextual needs, such as dietary restrictions or accessibility needs.
Better expense management. Companies benefit from AI connecting to corporate expense platforms to assist in travel cost management from the time booking is approved through the automatic processing of receipts. AI agents are able to reduce friction through the automatic categorization of expenses, alerting users to policy violations in real-time, and creating master reports of expenses.
Prediction at scale. According to Choi & Kim, AI watches flight schedules, weather, and local events and delivers proactive alerts and suggested alternative options. GPS tools facilitate more accurate travel forecasting, while AI-powered travel agents automatically follow the travel policy and understand suppliers’ routes and external factors.
Improved eco-practices. AI generates optimized flight paths, while also reducing contrails that contribute to 35% of aviation warming for the planet. AI agents are also able to promote sustainable travel by making eco-friendly accommodation suggestions and calculating carbon footprints.
Safety and security guaranteed. AI can also extend safety and security by delivering value-added risk management by predicting a range of threats from the weather to impacts from geopolitical disruptions, as Milton states, and enhancing airport security through biometric identification. AI agents maintain the ability to assess real-time conditions at potential destinations and offer alternative suggestions.
Enhanced accessibility and inclusion. In the industry of travel planning around disability travel, customer satisfaction still ranked at 82.5%, but technology can bring it up. For instance, AI translators provide real-time translations of indicated languages and point to specialized resources to enhance assistance for any differently-abled travellers.
Support without limits. Tourism is a global enterprise and must accommodate any last-minute changes or rebookings in any time zone of coverage, making full-time support critical. AI travel assistants do more than simply offer support; they complete complicated multi-step business transactions, and at the same time, offer proactive support to resolve disruptions.
Now that the critical role of artificial intelligence in tourism is clear, let’s focus on specialized AI solutions for this industry that have disrupted the market these days - AI travel agents.
What is an AI travel agent?
An AI agent for travel is a fully autonomous system that can execute considerably complex and long travel-related tasks through natural language interaction (e.g., search assistance, booking processing, etc.). This focused AI for travel agents has access to programming interfaces (APIs), the web, and third-party applications to autonomously complete entire transactions as reasoning-based problem solvers that adapt to a change in environment.
These agents use conversational interfaces that replace the conventional manner of search interfaces. For instance, the new agentic function of Google's Lens can "take a picture of virtually anything and we can build an itinerary around it," according to Google. These features are very useful for tourists to get information, which saves them time and helps them focus on their destination experience.
The coming shift toward autonomous networks (as Starkov shared with CNBC) enables them to do all of the research, planning, and booking vacations autonomously. This comes as a result of the complex dance of varied components of AI agents.
Key elements of AI travel agents
In the basic definition of an AI agent’s structure, there are several obvious elements. Surely, an agent cannot serve customers without natural language processing with multi-language support and a convenient chat interface.
Additionally, it should adapt and learn from previous conversations and have access to real-time data to provide objective responses. And finally, an AI agent would be no use if it didn’t automate tasks for customers and businesses, and didn’t personalize its answers at least to some level.
However, to understand the complex internal workflows of an AI agent, let’s dive into some more detail. We'll use the article from Cole Stryker on IBM resources to break down the major elements of AI agent infrastructure in a technical way.
Element 1. Intelligent perception and input processing.
AI travel agents use advanced perception modules that take in and decode information from a variety of sources, which can include user requests, system logs, structured API data, and real-time sensor readings from many travel systems. This module is capable of natural language processing and advanced AI techniques such as speech-to-text, sentiment analysis, and entity recognition to clean, process, and organize raw data into usable formats.
Element 2. Advanced planning and task decomposition.
Agents don’t just react to user input but consider many factors. For instance, the planning module of an AI booking agent breaks down complex travel itineraries into mini, routine steps while anticipating the relationship between bookings, transfers, and reservations. Then they use logic and machine learning models to create the best courses of action, planning for uncertain future events, and use a multi-agent systems approach to negotiate with travel suppliers.
Element 3. Dynamic memory and learning systems.
AI travel agents use short-term memory for session-based context, with the ability to recall recent conversations, keeping continuity throughout the booking processes. They also have long-term memory as a structured knowledge base (repository), vector embeddings, and historical data for personalization based on prior travel preferences and company policy. The learning module perpetually analyzes past interaction data to identify patterns and improve forecasting.
Element 4: Complex reasoning and decision-making.
The reasoning module is the cognitive intelligence of AI travel agents built on complex paradigms such as ReAct (Reasoning and Action) or ReWOO (Reasoning Without Observation) to weigh multiple paths of solutions based on performance outcomes. Goal-based AI booking assistants focus on specific travel objectives, while utility-based agents aim at the best possible outcomes from utility functions, which is necessary for increasingly complex tasks such as automated itinerary optimization, policy compliance verification, and more.
Element 5: Execution of independent action context and use of tools.
The action module executes AI agents' choices in the form of calling tools that enable agents to interact with external APIs, datasets, automation systems, and physical environments, so agents can make bookings, facilitate payments, and coordinate with numerous suppliers at once. This module also facilitates multi-agent communication, enabling agents to work with structured tools that enable access to information in real-time beyond the training data.
Since agentic AI is a relatively new phenomenon, many still confuse agents with travel management apps that have the basic AI chatbot functionality. However, they differ greatly.
Difference between basic chatbots and AI agents
Comparing an AI agent vs a chatbot in the travel industry is like comparing an autonomous car that can drive you on its own, and an interactive map that just directs you to the places to go to reach some goals (or solve some issues).
Capabilities and reasoning differ a lot. Chatbots use rules-driven dialogues and provide scripted answers based on the internal knowledge base. Meanwhile, AI agents can reason independently, ground answers in related knowledge and content, and adapt, learn, and grow beyond the prescribed rules, as determined by Cagle & Ahmed.
Training requirements are distinct, too. Basic chatbots require training hundreds of inquiries to understand where to apply natural-language requests, causing a significant upfront commitment to develop their dialogue configurations. Conversely, AI agents don't require rule-based dialogs, and all the configuration needed is significantly less invasive, which means you can launch AI agent tools faster.
Conversational flow control gives another aspect. Travel chatbots offer prescriptive, structured conversation paths where the business is in full control of the conversation outcomes. AI agents can allow for independent control of the conversation based on autonomous dialogue capabilities, allowing for more dynamic human-user agency to meet the user's needs in real-time.
Implementation complexity requires attention, as well. Basic chatbots require extensive amounts of programming related to decision trees, utterance training, and the manual configuration of pathways to responses. In contrast, AI agents more easily integrate into existing business processes, are easier to deploy, and allow for faster implementation across enterprise settings.
The very autonomy and intelligence differ. A basic travel AI chatbot simply doesn't think. They are uniform machines that perform programmed paths by predetermined rules. This said, AI agent architecture allows them to observe their environments, understand the material, evaluate their actions, and perform actions to achieve goals within their bounded context.
The greatest distinction is chatbot/AI agent use cases. Basic chatbots are effective as a touchpoint with customers within front-facing situations, when they expect a very standard and uniform response to regular questions, as Li & Zhang state. Meanwhile, AI agents are best in back-facing or employee-facing scenarios and complex business processes that require reasoning and context.
Such a great deal of automation and self-governance has distinct outcomes, both for the travelers and the travel agents alike. Let’s break them down.
Key impacts of AI agents in travel
As Mindtrip CEO Andy Moss explains, "Rather than going to Google, where you do one search, and then you do another... You can just get into everything at once with an AI travel agent". What are the results that tourists and OTAs get from this ability?
Impact on travelers
AI agents use the vast wealth of databases now available on everything from cultural sites to natural wonders to food experiences to cultural events around the world, leveraging the current and past traveler preferences. With each of these data points, agents take the act of planning a trip, which formerly took hours of research, and then use AI travel booking sequences to eliminate human intervention and complete all transactions on their own. Which, surely, has a great positive impact:
Customized itineraries based on what the traveler wants - without any explanations needed, as the data is already there to analyze (e.g., museum tours and cultural shows for history lovers, hiking trails and water sports recommendations).
Convenience is enhanced as an AI agent assists with an instant trip plan and provides complete booking capabilities for hotels, restaurants, and activities on a platform that gathers everything together.
Real-time flexibility is a big pro.As NYU Professor Jukka Laitamaki, stated, “Whatever real-time changes may happen... You don’t have to call anybody - you just put it into the system..."
Access in one place also adds to the good experience. Discovery, planning, and booking are all done within one service or platform.
However, with AI travel agents, it’s not always possible to cover all the bases. Some negative effects also occur:
AI-generated recommendations don't capture the richness of human interactions and personal information that a human agent or local with direct knowledge of a destination can provide.
Data collection raises questions about who has access to that information and how it'll be used, which may cause traveler distrust.
Also, a traveler's dependence on AI reduces their ability to navigate by themselves and problem-solve efficiently.
Having said that, the impacts on consumer behaviour demonstrate some contradictions that could be intriguing. Rather than being loyal to a brand, travelers follow a specific AI system to make travel arrangements. Optimization algorithms predominantly point visitors to the 'most easily processable' options while failing to make suggestions that speak to unique local experiences that are the richness of memorable travel.
Impact on OTAs
Artificial intelligence travel agents might bring some risks to the very existence of Online Travel Agencies. With industry leaders like Google’s CEO Sundar Pichai declaring that Gemini AI develops a "Universal AI agent that'll be useful in everyday life", their services might not be as necessary in the future as they are now. Such assumptions are caused by some factors:
Just like Google organic rankings favour brand names, AI agents learn that using established OTAs is a lesser risk, which creates a self-reinforcing trust loop.
Current API architectures that can support billions of daily calls become the backbone of the AI agent ecosystem, causing many companies to restructure their architecture fully.
Each AI-enabled transaction moving through the OTA pipelines becomes intelligence that cannot be replicated by suppliers.
Direct competition against suppliers is complicated, as hotels and airlines could simply visit the OTA equivalent to promote direct bids for AI agents' attention.
There are some potential risks to OTA website design. AI prefers text-density over minimal designs which may force a "return to 1990s" feeling with few visuals and an abundance of text (which may drive human users away).
As Eva Stewart of consultancy GSIQ shared about the competition disrupted by AI agents, "widespread adoption is slow," as many small and medium businesses have a limited ability to integrate AI because they have no infrastructure. However, companies like COAX can help cover the gap, creating online travel booking solutions for companies of practically any size, budget, and customer base. Additionally, we help you balance design and content to optimize both for your users and the AI agents crawling OTAs’ websites.
The battle for supremacy of AI agents is already shifting competitive landscapes. Richmond from CNBC explains the new ecosystem: "In a world where AI agents act on behalf of the traveler, those agents will likely be in constant communication with other virtual travel agents merely", simply crossing human expertise out of the list.
Nonetheless, not everyone who holds industry weight thinks we'll see a disruption. Airbnb CEO Brian Chesky warned not to think of AI agents as “the new Google" and noted that even these AI models are not proprietary. This means that even with the growing role of agents in the travel industry, there'll still be a place for the branded experience people strive for.
Top AI travel agents in 2025
To balance your unique offering with the convenience of agentic AI, you might be interested in using some efficient AI agent platforms that provide the ability to assist your travel-eager customers, automate processes, and drive convenience while you concentrate on making your destinations and packages exciting.
ChatGPT travel tool enables travel agencies to create branded no-code AI chatbots that provide customers with a more seamless and personalized experience while leaving trip planning to the platform. These agents automate inquiries and trip planning based on preferences and budget, and provide 24/7 multi-lingual support by training on your own data, like travel guides and policies. GPTBot offers seamless integration of your customer CRM, WhatsApp, Discord, and suppliers' API for real-time data, to offer consistently.
Expedia’s Romie AI agent is great for building end-to-end itineraries, updating you with delay or weather disruption notifications, and even connecting with the chat you travel your group trip with friends, family, or co-workers. It’s done through the integration with WhatsApp and iMessage, and advanced natural language processing.
TripGenie, developed by Trip.com is most popular in Asia now, but its influence is spreading. It boasts the ability to suggest entire trips with flights, accommodations, and activities, and can predict pricing fluctuations, also considering your past preferences. Even local insights are considered in advanced TripGenie’s multi-language reponses.
Navan helps you harness the power of generative AI as an agent for corporate travel, automating complex logistics and expense management to improve efficiency. It provides the automating og flight rebookings, hotel changes, invoice retrievals, and expense categorization. Automated workflows resolve inquiries without human intervention. Deep integration with corporate travel policies, booking engines, and expense platforms brings one travel and finance management process together.
Intercom aims to provide an all-in-one customer service platform with a highly effective AI chatbot builder that is perfect for enhancing the traveler support experience. Most customer queries are resolved by Fin AI, and it can help human agents by presenting relevant context and processes for challenging traveller interactions. Omni-channel support with native integrations for WhatsApp, email, and SMS, in addition to a service suite of product tours and surveys, makes it one of the best AI agent platforms.
Chatbase helps businesses create and add an AI-based chatbot to their website to answer customer inquiries. It can automate answers to routinely asked questions, based on training using data supplied by the user, supporting 80+ languages. Chatbase is embedded directly in a company's website and can be integrated with many commonly used business tools; however, it is not specifically developed for advanced travel functionalities like bookings.
Botpress is a powerful and developer-centric platform to build highly sophisticated and scalable enterprise-level virtual agent AI. It can automate complex workflows and business-relevant customer interactions using sophisticated logic created using visual flow charts. Botpress has an easy-to-use API and SDK to make the integration with your wider array of business applications and tools seamless and simple.
Hopper functions as a consumer-facing AI travel agent that anticipates price trends and protects consumers from price fluctuations for flights and hotels. It automates future price prediction, generates the optimal time to send a booking alert, and generates a "Price Freeze" feature to lock in pricing, as well as automatically rebook are all disruption features. The platform utilizes deep integrations with airline digital solutions and hotel inventory to aid with real-time price changes and notifications.
With such a great variety of AI agent software available, you might still face the limitations of the off-the-shelf agent builders. Some don’t integrate with the systems you need, and others have very strict plans for the number of inquiries. Besides, it takes a great deal of technical proficiency to understand and apply the documentation to understand how to set the necessary integrations to create an AI for ticket booking or package creation.
To make your agent AI tool fully connected, the COAX teams apply all their years of experience with AI software development and integration to establish secure and robust API integrations. We use the best practices of data preparation to enrich your agent with the updated information to assist your customers and drive revenues by connecting with your supplier network efficiently.
Examples of companies that successfully implemented AI agents
The list of travel agencies that implement agentic AI is growing rapidly. Let’s look at some of the successful cases of such implementations.
Start-up Mindtrip is showcasing this evolution by using a generative AI itinerary generator that allows users to create complex offerings from simple text inputs. They also include travel booking AI automation connected to their partner websites. Established companies are also adapting - Expedia launched an AI assistant, Romie, to help with the planning of group trips.
Some OTAs are using AI agents to improve their operations. Airbnb is using AI internally – their customer service agent reportedly reduced human involvement by 15% using 13 different models that were trained on thousands of conversations.
Booking.com is utilizing AI agents from OpenAI to create vertical agents. The Booking AI trip planner agent aims to create a more transparent and personalized connected trip by intelligently integrating travel components such as accommodations, flights, etc. The company considers agentic AI a meaningful contributor to their growth.
Meanwhile, HotelPlanner is using AI voice agents to handle the massive traffic of customer service inquiries and bookings from its customers. Before this virtual travel booking agent AI, they could handle fewer than 25,000 calls daily, and now their capacity has risen to 45,000. AI has allowed the company to massively scale, while allowing human agents to pay attention to enhance bookings that are more complex and valuable.
Qatar Airways introduced an AI called Sama, which is a digital cabin crew that can assist travelers through voice and chat. The company lets AI manage bookings of personalized itineraries and enhance the experience directly within their airline reservation system software.
How to get ready for AI agents implementation
To create AI agents that work reliably and truly as you expected them to (or even better), you should follow a structured approach. One great example is the "Traveller" agent developed by Schiaffino and Amandi, a booking AI that effectively recommends package holidays.
It should all start with thorough preparation - let’s see how it’s done in practice:
Define scope and goals.
It's important to identify what your AI travel agent should do: help book travel, help manage itinerary changes, support customers, or offer a combination of services? Make sure that you identify which capabilities are related to specific business goals; e.g., increase conversion rate or generate sales from your virtual travel agent. This ensures the technology provides real value rather than simply answering easy questions.
Examine data structure.
Evaluate your existing customer data, booking history, and historical travel inventory to ensure that you have sufficient data to train your AI booking agent or trip planning assistant. You need some level of organization and cleaning of cited data if using customer preferences and customers' demographic data for training, similar to the way that Traveller identified potential hybrid recommendations, which included customer characteristics, behaviour, and preferences across multiple sources of data.
Construct your interactions to feel organic and familiar, especially for travelers who may be stressed or trying to act quickly, requiring clear and concise actions. Train your agent with real travel queries from historical data so you can improve accuracy when trying to decipher the varied ways different people ask for their requests. Include a proactive messaging capability, where your AI virtual agent can send flight reminders, price alerts, and personal upgrade recommendations based on booking patterns.
Build a recommendation engine.
Create filtering mechanisms, content-based recommendations, and demographic profiling to gain from the strengths of all three aspects. For example, the Traveller solution used a mix of collaborative filtering and content-based recommendations to create an AI to plan vacations, avoiding making recommendations that are too narrow or outdated.
Connect to real-time data sources.
Link your agent with airlines, hotels, car rentals, and travel APIs so that you have the best possible, current information and facilitate and facilitate transactions. Ensure the connection across travel platforms is seamless so that agents can give a full-service experience without asking for a passenger to switch providers/ platforms.
Implement escalation protocols.
Design clear handoff protocols for situations that involve complex decisions, empathy, or specialist information where a human agent needs to provide service beyond a simple change. If a situation is escalated, provide the human agent with context, so they don't have to have the travelers reexplain situations, time delays, and provide full customer service.
Apart from the main strategy, there are a lot of moving parts to keep an eye on. To ensure quality and optimize your virtual travel agent, we put together some useful tips.
Practical tips for travel AI agent optimization
With AI, guessing is not an option. You need some proven practices to monitor your agent’s performance.
Tip 1. Test like your customers will.
Test with real customer scenarios - "change my bus ride to Thursday", "find me hostels near Prado Museum which cost less than $100". Use as diverse options as possible - for instance, Schiaffino and Amandi tested Traveller with 25 real users using a variety of holiday styles to establish that their hybrid approach performed better than employing a single method.
Tip 2. Monitor what really matters.
Don't just count conversations - record if people actually book after speaking to your tool. Even the best AI travel agent cannot boost your profits if you don’t continuously monitor the right itinerary KPIs. Schiaffino and Amandi found that their system gave better prediction values for family holidays in Fortaleza when compared to pure collaborative or content-based systems alone. The focus should be on conversion rates and customer satisfaction, not just on how many chats happen.
Tip 3. Verify everything, literally.
Test your agent for hallucinations and implement corrections in either your data sources or the algorithms that power its workflows. Confirm things such as address, hours of operation, and price on official or reputable travel booking sites. Also, regularly update your data sources, including your suppliers’ booking sites, review websites, and maps to check validity.
Tip 4. Continue learning and adapting.
Launch with the mindset of continuous assessment of what works and what doesn’t in the context of real customers using your AI agent applications. Successful tuning and continuous optimization require you to frequently update your agent based on actual patterns or use, not theoretical improvements.
These nuances are what we cover completely at COAX. When our teams deliver custom software development for travel businesses, we follow this end-to-end approach, where we kick off with a comprehensive review of your goals and technical limitations, prepare the data sources for your agent (whether you want to use AI hotel booking, itinerary planning, customer support, or other workflows), and implement scalable solutions with self-learning and self-guidance mechanisms that reduce the need for technical support.
Still, any AI travel agency with a high level of automation requires constant ongoing support. This is why we don’t leave you flying support - you can always count on our post-launch monitoring and enhancement of your AI virtual agents, together with updating data sources for truthfulness and up-to-dateness, fine-tune your AI model to remove hallucinations, and track vital metrics to ensure its stable, secure functioning that moves your business forward.
FAQ
What is the most common AI agent meaning?
IBM defines an AI agent as a self-directed, autonomous system that completes tasks by developing workflows with the tools at hand. Taking into account all the features of decision-making, problem-solving, interacting with the environment, and taking action, agents can solve complex enterprise tasks (which could be software design, IT automation, code generation, or conversational assistance) employing LLM techniques so that a user can understand and connect to external task-performing tools.
What are the most widespread AI agent types in the travel industry?
A simple reflex agent with pre-defined rules can be used in automated check-in kiosks.
Model-based reflex agents - AI for flight booking that tracks passenger history.
Goal-based agent, like a trip planning virtual travel agent that provides itineraries to reach travel objectives.
Utility-based agents: dynamic systems that provide pricing based on cost and availability.
Learning agents, such as ChatGPT Travel Agent, keep improving their responses from interactions.
Are AI agents fully autonomous, and how does it work for tourism?
Even the best AI travel agent follows rules and goals laid out by a human. The agent’s behavior is shaped by three aspects, according to Gutowska:
Developers who design and train systems.
Access provided by the deployment teams.
The definite goals users set.
Integrations impact, too. For instance, a vacation planning agent breaks down a vacation into subtasks, each depending on external sources: connecting to the weather database, accessing a local guide, or considering other agents to optimize trip conditions.
What are the risks of an AI booking system?
The key risks include:
Data security weaknesses that could expose your personal information.
Payment information might be stolen if the transaction is intercepted.
Algorithm breakdowns affecting fairness in pricing, delays, or disruptions.
Breaches of compliance obligations through human error
Misconfiguration or weaknesses through third-party integrations.
How does COAX ensure the security of your AI agent development services?
Our engineers employ multiple security layers, including OAuth 2.0/JWT authentication and end-to-end encryption. Additionally, COAX is ISO/IEC 27001:2022 certified for comprehensive management of security, risk assessment, and monitoring security risks. We also have a confirmed ISO 9001 certification that ensures optimal processes for quality. Collectively, these frameworks help build trust with customers, show compliance with regulations, and protect travel data that involves booking information, IDs, and payment details.
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